Journal articles: 'Heating and cooling networks'

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Zeh, Robin, Björn Ohlsen, David Philipp, David Bertermann, Tim Kotz, Nikola Jocić, and Volker Stockinger. "Large-Scale Geothermal Collector Systems for 5th Generation District Heating and Cooling Networks." Sustainability 13, no. 11 (May 27, 2021): 6035. http://dx.doi.org/10.3390/su13116035.

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Low temperature district heating and cooling networks (5GDHC) in combination with very shallow geothermal energy potentials enable the complete renewable heating and cooling supply of settlements up to entire city districts. With the help of 5GDHC, heating and cooling can be distributed at a low temperature level with almost no distribution losses and made useable to consumers via decentralized heat pumps (HP). Numerous renewable heat sources, from wastewater heat exchangers and low-temperature industrial waste heat to borehole heat exchangers and large-scale geothermal collector systems (LSC), can be used for these networks. The use of large-scale geothermal collector systems also offers the opportunity to shift heating and cooling loads seasonally, contributing to flexibility in the heating network. In addition, the soil can be cooled below freezing point due to the strong regeneration caused by the solar irradiation. Multilayer geothermal collector systems can be used to deliberately generate excessive cooling of individual areas in order to provide cooling energy for residential buildings, office complexes or industrial applications. Planning these systems requires expertise and understanding regarding the interaction of these technologies in the overall system. This paper provides a summary of experience in planning 5GDHC with large-scale geothermal collector systems as well as other renewable heat sources.

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Heller, Daniel, Alex Heller, Samir Moujaes, Shelley J. Williams, Ryan Hoffmann, Paul Sarkisian, Kaveh Khalili, et al. "Research: Testing of a Novel Portable Body Temperature Conditioner Using a Thermal Manikin." Biomedical Instrumentation & Technology 50, no. 5 (September 1, 2016): 336–48. http://dx.doi.org/10.2345/0899-8205-50.5.336.

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Abstract A battery-operated active cooling/heating device was developed to maintain thermoregulation of trauma victims in austere environments while awaiting evacuation to a hospital for further treatment. The use of a thermal manikin was adopted for this study in order to simulate load testing and evaluate the performance of this novel portable active cooling/heating device for both continuous (external power source) and battery power. The performance of the portable body temperature conditioner (PBTC) was evaluated through cooling/heating fraction tests to analyze the heat transfer between a thermal manikin and circulating water blanket to show consistent performance while operating under battery power. For the cooling/heating fraction tests, the ambient temperature was set to 15°C ± 1°C (heating) and 30°C ± 1°C (cooling). The PBTC water temperature was set to 37°C for the heating mode tests and 15°C for the cooling mode tests. The results showed consistent performance of the PBTC in terms of cooling/heating capacity while operating under both continuous and battery power. The PBTC functioned as intended and shows promise as a portable warming/cooling device for operation in the field.

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Charlebois, Daniel A., Kevin Hauser, Sylvia Marshall, and Gábor Balázsi. "Multiscale effects of heating and cooling on genes and gene networks." Proceedings of the National Academy of Sciences 115, no. 45 (October 19, 2018): E10797—E10806. http://dx.doi.org/10.1073/pnas.1810858115.

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Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits inSaccharomyces cerevisiae. We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.

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Brennenstuhl, Marcus, Robin Zeh, Robert Otto, Ruben Pesch, Volker Stockinger, and Dirk Pietruschka. "Report on a Plus-Energy District with Low-Temperature DHC Network, Novel Agrothermal Heat Source, and Applied Demand Response." Applied Sciences 9, no. 23 (November 23, 2019): 5059. http://dx.doi.org/10.3390/app9235059.

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District heating and cooling networks can pose the possibility of including a variety of renewable energy sources as well as waste heat into a district’s heat supply concept. Unfortunately, low demand densities as they increasingly occur through higher building energy standards and in rural areas render conventional heating and cooling networks inefficient. At the same time, power-to-heat is becoming more and more important to make use of a larger amount of renewable energy sources on the electrical side by providing more flexibility by means of demand response and demand-side management. Within this work, a rural Plus-Energy settlement is presented addressing those topics by a low-temperature district heating and cooling network connected to a novel agrothermal collector supplying 23 residential buildings with decentralized heat pumps and PV systems. The collector, the network, and six of the buildings are equipped with comprehensive monitoring equipment. Within those buildings, forecast and optimization algorithms are implemented to adapt their heat pump operation to enable an increase of self-consumption, to include flexible electricity tariffs, and also to participate in power markets. Thereby, for the low-temperature district heating and cooling network, it has been shown that the concept can operate in the future at competitive heat costs. On the building level, up to 50% of cost savings could be achieved under ideal conditions with the optimization of the self-consumption of PV electricity. However, to ensure optimal results, the individual system components have to be dimensioned for this task.

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del Hoyo Arce, Itzal, Saioa Herrero López, Susana López Perez, Miika Rämä, Krzysztof Klobut, and Jesus A. Febres. "Models for fast modelling of district heating and cooling networks." Renewable and Sustainable Energy Reviews 82 (February 2018): 1863–73. http://dx.doi.org/10.1016/j.rser.2017.06.109.

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Sporleder, Maximilian, Max Burkhardt, Thomas Kohne, Daniel Moog, and Matthias Weigold. "Optimum Design and Control of Heat Pumps for Integration into Thermohydraulic Networks." Sustainability 12, no. 22 (November 12, 2020): 9421. http://dx.doi.org/10.3390/su12229421.

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Germany has become one of the leading players in the transformation of the electricity sector, now having up to 42% of electricity coming from renewable sources. However, the transformation of the heating sector is still in its infancy, and especially the provision of industrial process heating is highly dependent on unsustainable fuels. One of the most promising heating technologies for renewable energies is power-to-heat, especially heat pump technology, as it can use renewable electricity to generate heat efficiently. This research explores the economic and technical boundary conditions regarding the integration of heat pumps into existing industrial thermohydraulic heating and cooling networks. To calculate the optimum design and control of heat pumps, a mixed-integer linear programming model (MILP) is developed. The model seeks the most cost-efficient configuration of heat pumps and stratified thermal storage tanks. Additionally, it optimizes the operation of all energy converters and stratified thermal storage tanks to meet a specified heating and cooling demand over one year. The objective function is modeled after the net present value (NPV) method and considers capital expenditures (costs for heat pumps and stratified thermal storage tanks) and operational expenditures (electricity costs and costs for conventional heating and cooling). The comparison of the results via a simulation model reveals an accuracy of more than 90%.

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Kim, Min-Hwi, Deuk-Won Kim, Dong-Won Lee, and Jaehyeok Heo. "Experimental Analysis of Bi-Directional Heat Trading Operation Integrated with Heat Prosumers in Thermal Networks." Energies 14, no. 18 (September 17, 2021): 5881. http://dx.doi.org/10.3390/en14185881.

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District cooling and heating methods that can utilize highly efficient heat pumps and various unused new and renewable types of energy are required to achieve low carbon emissions and zero energy usage in buildings and community units. The technical requirements for the implementation of decentralized thermal networks and heat trading are increasing, both for thermal networks in new buildings and for those remodeled based on existing centralized thermal networks. In this study, a conventional centralized thermal network was implemented as a decentralized thermal network and the possibility of heat prosumers feeding thermal networks was demonstrated experimentally. A real-scale plant was constructed by employing unused thermal energy facilities as prosumers in a school and childcare center based on the existing small-scale block heating and cooling thermal network. The decentralized thermal network and heat prosumer concepts were proven through operation experiments performed on the constructed system in summer and winter. An economic benefit can be achieved by increasing the peak power cost. The experimental results also showed that the proposed bi-directional heat trading reduced carbon emissions by 12.7% compared with conventional centralized thermal systems.

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Njawah Achiri, Humphrey Mokom, Vaclav Smidl, Zdenek Peroutka, and Lubos Streit. "Least Squares Method for Identification of IGBT Thermal Impedance Networks Using Direct Temperature Measurements." Energies 13, no. 14 (July 21, 2020): 3749. http://dx.doi.org/10.3390/en13143749.

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State-of-the-art methods for determining thermal impedance networks for IGBT (Insulated Gate Bipolar Transistor) modules usually involves the establishment of the relationship between the measured transistor or diode voltage and temperature under homogenous temperature distribution across the IGBT module. The junction temperature is recomputed from the established voltage–temperature relationship and used in determining the thermal impedance network. This method requires accurate measurement of voltage drop across the transistors and diodes under specific designed heating and cooling profiles. Validation of the junction temperature is usually done using infrared camera or sensors placed close to the transistors or diodes (in some cases and open IGBT module) so that the measured temperature is as close to the junction as possible. In this paper, we propose an alternative method for determining the IGBT thermal impedance network using the principles of least squares. This method uses measured temperatures for defined heating and cooling cycles under different cooling conditions to determine the thermal impedance network. The results from the proposed method are compared with those obtained using state-of-the-art methods.

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Liu, Lan Bin, Ai Juan Zou, Jia Jun Liao, and Ya Meng Liu. "The Optimization of Large Scale Heating and Cooling Network." Applied Mechanics and Materials 525 (February 2014): 616–20. http://dx.doi.org/10.4028/www.scientific.net/amm.525.616.

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It is a question if large scale heating/cooling network could be applied in China economically, environmental protection and energy saving. Toward this question, this paper studied the optimization of large scale heating and cooling network. The components of the system are analyzed and modeled in detail including heating/cooling source, heating/cooling users and transmit system. And the system can be optimized through the way of Life Cycle Cost, which compared the cost of large scale heating and cooling system with conventional system. The optimization also takes efficiency and economy in to consideration.

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Cristea, Mariana, Sorin Ibanescu, Constantin N. Cascaval, and Dan Rosu. "Dynamic Mechanical Analysis of Polyurethane-Epoxy Interpenetrating Polymer Networks." High Performance Polymers 21, no. 5 (September 8, 2009): 608–23. http://dx.doi.org/10.1177/0954008309339940.

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A series of semi-interpenetrated polymer networks based on bisphenol A epoxy resin and polyurethane was synthesized by sequential procedure. The molecular dynamics of polyurethane incorporated in the resin network with increasing amounts of resin was followed by dynamic mechanical analysis. All phenomena that concur in the material are evaluated by cross-examination of the storage modulus ( E'), loss modulus ( E'') and loss factor (tan δ) variation with temperature. Complex aspects were elucidated in consecutive heating-cooling-heating cycles and by calculating the apparent activation energy of relaxations in multiplex experiments.

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Moradzadeh, Arash, Amin Mansour-Saatloo, Behnam Mohammadi-Ivatloo, and Amjad Anvari-Moghaddam. "Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings." Applied Sciences 10, no. 11 (May 31, 2020): 3829. http://dx.doi.org/10.3390/app10113829.

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Nowadays, since energy management of buildings contributes to the operation cost, many efforts are made to optimize the energy consumption of buildings. In addition, the most consumed energy in the buildings is assigned to the indoor heating and cooling comforts. In this regard, this paper proposes a heating and cooling load forecasting methodology, which by taking this methodology into the account energy consumption of the buildings can be optimized. Multilayer perceptron (MLP) and support vector regression (SVR) for the heating and cooling load forecasting of residential buildings are employed. MLP and SVR are the applications of artificial neural networks and machine learning, respectively. These methods commonly are used for modeling and regression and produce a linear mapping between input and output variables. Proposed methods are taught using training data pertaining to the characteristics of each sample in the dataset. To apply the proposed methods, a simulated dataset will be used, in which the technical parameters of the building are used as input variables and heating and cooling loads are selected as output variables for each network. Finally, the simulation and numerical results illustrates the effectiveness of the proposed methodologies.

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Estakhr, Javad, Mohsen Simab, and Taher Niknam. "Security Analysis of Hybrid Multi-Carrier Energy Systems." Sustainability 13, no. 6 (March 12, 2021): 3102. http://dx.doi.org/10.3390/su13063102.

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Multi-carrier energy systems (MCESs) provide collaboration between various kinds of energy carriers to supply the electricity, heating, and cooling demands. With the widespread use of MCESs in recent years, the security assessment of energy systems has attracted the attention of many contemporary researchers. However, the complexity of an MCES, including electrical, natural gas, and district heating networks, and different uncertainties imposes vast challenges to keep a safe operation energy supply. In this paper, a systematic methodology for the security analysis of MCESs is presented. For this purpose, considering electrical, natural gas, and district heating networks, an integrated model of energy systems is introduced. The security analysis of this framework is evaluated using some indices. In this approach, two well-known performance indices, including power performance index (PIP) and voltage performance index (PIV), are used to analyze the electrical networks’ security. Besides, the concept of Energy not supplied (ENS) is used for natural gas and district heating networks. In this regard, security analysis of a typical MCES including the IEEE 14-bus electrical network, the IEEE 30-bus electrical network, 20-node Belgian natural gas network, and 14-node district heating network is examined. The applicability of the proposed technique will be proven using comprehensive simulation analysis.

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Tunzi, Michele, Matthieu Ruysschaert, Svend Svendsen, and Kevin Michael Smith. "Double Loop Network for Combined Heating and Cooling in Low Heat Density Areas." Energies 13, no. 22 (November 20, 2020): 6091. http://dx.doi.org/10.3390/en13226091.

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This study investigated a double loop network operated with ultra-low supply/return temperatures of 45/25 °C as a novel solution for low heat-density areas in Denmark and compared the proposed concept with a typical tree network and with individual heat pumps to each end-users rather than district networks. It is a pump-driven system, where the separate circulation of supply and return flow increased the flexibility of the system to integrate and displace heating and cooling energy along the network. Despite the increased use of central and local water pumps to operate and control the system, the simulated overall pump energy consumption was 0.9% of the total energy consumption. This was also an advantage at the design stage as the larger pressure gradient, up to 570 Pa/m, allowed minimal pipe diameters. In addition, the authors proposed the installation of electrically heated vacuum-insulated micro tanks of 10 L on the primary side of each building substation as a supplementary heating solution to meet the comfort and hygiene requirements for domestic hot water (DHW). This, combined with supply water circulation in the loop network, served as a technical solution to remove the need for bypass valves during summer periods with no load in the network. The proposed double loop system reduced distribution heat losses from 19% to 12% of the total energy consumption and decreased average return temperatures from 33 °C to 23 °C compared to the tree network. While excess heat recovery can be limited due to hydraulic issues in tree networks, the study investigated the double loop concept for scenarios with heat source temperatures of 30 °C and 45 °C. The double loop network was cost-competitive when considering the required capital and operating costs. Furthermore, district networks outperformed individual heat pump solutions for low-heat density areas when waste heat was available locally. Finally, although few in Denmark envisage residential cooling as a priority, this study investigated the potential of embedding heating and cooling in the same infrastructure. It found that the return line could deliver cold water to the end-users and that the maximum cooling power was 1.4 kW to each end-user, which corresponded to 47% of the total peak heat demand used to dimension the double loop network.

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Vandermeulen, Annelies, Bram van der Heijde, and Lieve Helsen. "Controlling district heating and cooling networks to unlock flexibility: A review." Energy 151 (May 2018): 103–15. http://dx.doi.org/10.1016/j.energy.2018.03.034.

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Liu, Lan Bin, Ai Juan Zou, Jia Jun Liao, and Yu Fei Ma. "The Case Study on Optimization of Large Scale Heating and Cooling Network." Applied Mechanics and Materials 525 (February 2014): 611–15. http://dx.doi.org/10.4028/www.scientific.net/amm.525.611.

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This paper discussed the optimization of two cases of large scale heating and cooling system. One is the heating/cooling system with heating/cooling source with distance from load center, the other is the heating/cooling system with heating/cooling source located in the load center. The affect of the temperature difference between supply and return water, load density and the price of energy towards optimized radius and max radius was discussed using the existed model. The optimized radius and appropriate system parameters are got. It is a reference to the designers and operators related.

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Lagoeiro, Henrique, Akos Revesz, Gareth Davies, Graeme Maidment, Daniel Curry, Gareth Faulks, and Michal Murawa. "Opportunities for Integrating Underground Railways into Low Carbon Urban Energy Networks: A Review." Applied Sciences 9, no. 16 (August 14, 2019): 3332. http://dx.doi.org/10.3390/app9163332.

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Cities demand vast amounts of energy for their everyday operation, resulting in significant degradation of energy in the form of heat in the urban environment. This leads to high cooling requirements in cities, while also presenting the opportunity to reuse such waste heat in order to provide low-carbon heating for buildings and processes. Among the many potential energy sources that could be exploited in urban areas, underground railway tunnels are particularly attractive, as the operation of the trains produce considerable amounts of heat throughout the year. This paper reviews how secondary energy sources in urban areas can be integrated into heating and cooling networks, with emphasis on underground rail tunnels. This involves investigating potential urban waste heat sources and the existing state-of-the-art technologies that could be applied to efficiently recover this secondary energy, as well as analyzing how district heating and cooling networks have been a key mechanism to allow for a smooth transition from current fossil fuel based to future low-carbon energy sources.

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Moustakidis, Serafeim, Ioannis Meintanis, Nicos Karkanias, George Halikias, Elise Saoutieff, Pierre Gasnier, Javier Ojer-Aranguren, et al. "Innovative Technologies for District Heating and Cooling: InDeal Project." Proceedings 5, no. 1 (February 15, 2019): 1. http://dx.doi.org/10.3390/proceedings2019005001.

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The paper discusses the outcomes of the conference organized by the InDeal project. The conference took place on 12 December 2018 in Montpellier as part of the EnerGaia energy forum 2018. A holistic interdisciplinary approach for district heating and cooling (DHC) networks is presented that integrates heterogeneous innovative technologies from various scientific sectors. The solution is based on a multi-layer control and modelling framework that has been designed to minimize the total plant production costs and optimize heating/cooling distribution. Artificial intelligence tools are employed to model uncertainties associated with weather and energy demand forecasts, as well as quantify the energy storage capacity. Smart metering devices are utilized to collect information about all the crucial heat substations’ parameters, whereas a web-based platform offers a unique user environment for network operators. Three new technologies have been further developed to improve the efficiency of pipe design of DHC systems: (i) A new sustainable insulation material for reducing heat losses, (ii) a new quick-fit joint for an easy installation, and (iii) a new coating for reducing pressure head losses. The results of a study on the development and optimization of two energy harvesting systems are also provided. The assessment of the environmental, economic and social impact of the proposed holistic approach is performed through a life cycle analysis. The validation methodology of the integrated solution is also described, whereas conclusions and future work are finally given.

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Kim, Min-Hwi, Deuk-Won Kim, Gwangwoo Han, Jaehyeok Heo, and Dong-Won Lee. "Ground Source and Sewage Water Source Heat Pump Systems for Block Heating and Cooling Network." Energies 14, no. 18 (September 8, 2021): 5640. http://dx.doi.org/10.3390/en14185640.

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The demand for district heating and cooling systems in block units with a heat pump that utilizes various unused energy sources for energy supply has been increasing. This study investigated experimentally the ground source heat pump (GSHP) and sewage water source heat pump (SWSHP) facilities used in block cooling and heating networks. Then, a heat pump performance prediction model was derived for utilization in future designs. Operational data for heating and cooling energy supply from an experimental site were investigated for the period between 2018 and 2020. During the cooling season, the coefficient of performance (COP) of the GSHP was approximately 4.1, and that of the SWSHP was approximately 2.9. The cooling performance of the SWSHP gradually decreased because of the fouling. The COP of the GSHP and SWSHP during the heating season was approximately 3.6 and 3.4, respectively. The results also demonstrated that, if fouling in the SWSHP can be prevented or reduced, the acquired COP can be similar to that of the GSHP. The derived prediction model serves as a good reference for engineers who require information on the performance of field operations.

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Madan, Violeta, and Ingo Weidlich. "Investigation on Relative Heat Losses and Gains of Heating and Cooling Networks." Environmental and Climate Technologies 25, no. 1 (January 1, 2021): 479–90. http://dx.doi.org/10.2478/rtuect-2021-0035.

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Abstract The integration of district heating (DH) and cooling (DC) in the sustainable energy system of the future requires a significant reduction in operating temperatures. Supply temperatures below 70 °C are required for new 4th Generation DH. Main benefits are the use of low exergy heat sources and the reduction of heat losses. The reduction of heat losses is achieved by reducing the driving temperature difference between the medium pipe and the ground. The decrease of the return temperature level is limited by the consumer behaviour and the ground temperature level. As a consequence, the reduction of the supply temperature is accompanied by a reduction of the maximum transmittable heat flow. For energy efficiency and economic reasons, the relative heat losses are therefore an important design value for DH networks. The study proposes an approach to estimate the relative heat losses by using steady-state heat loss models and analyses the values for different DH generations. In particular, due to the rising of the near-surface soil temperature, the relative cold losses are also studied.

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Milčák, P., J. Konvička, and M. Jasenská. "Implementation of Biogas Stations into Smart Heating and Cooling Network." Technological Engineering 13, no. 1 (October 1, 2016): 26–27. http://dx.doi.org/10.2478/teen-2016-0008.

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Abstract The paper is aimed at the description of implementation of a biogas station into software environment for the „Smart Heating and Cooling Networks”. The aim of this project is creation of a software tool for preparation of operation and optimization of treatment of heat/cool in small regions. In this case, the biogas station represents a kind of renewable energy source, which, however, has its own operational specifics which need to be taken into account at the creation of an implementation project. For a specific biogas station, a detailed computational model was elaborated, which is parameterized in particular for an optimization of the total computational time.

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Todorov, Oleg, Kari Alanne, Markku Virtanen, and Risto Kosonen. "Aquifer Thermal Energy Storage (ATES) for District Heating and Cooling: A Novel Modeling Approach Applied in a Case Study of a Finnish Urban District." Energies 13, no. 10 (May 14, 2020): 2478. http://dx.doi.org/10.3390/en13102478.

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Aquifer thermal energy storage (ATES) combined with ground-source heat pumps (GSHP) offer an attractive technology to match supply and demand by efficiently recycling heating and cooling loads. This study analyses the integration of the ATES–GSHP system in both district heating and cooling networks of an urban district in southwestern Finland, in terms of technoeconomic feasibility, efficiency, and impact on the aquifer area. A novel mathematical modeling for GSHP operation and energy system management is proposed and demonstrated, using hourly data for heating and cooling demand. Hydrogeological and geographic data from different Finnish data sources is retrieved in order to calibrate and validate a groundwater model. Two different scenarios for ATES operation are investigated, limited by the maximum pumping flow rate of the groundwater area. The additional precooling exchanger in the second scenario resulted in an important advantage, since it increased the heating and cooling demand covered by ATES by 13% and 15%, respectively, and decreased the energy production cost by 5.2%. It is concluded that dispatching heating and cooling loads in a single operation, with annually balanced ATES management in terms of energy and pumping flows resulted in a low long-term environmental impact and is economically feasible (energy production cost below 30 €/MWh).

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Marijuan, Antonio Garrido, Roberto Garay, Mikel Lumbreras, Víctor Sánchez, Olga Macias, and Juan Perez Sainz De Rozas. "RELaTED Project: New Developments on Ultra-Low Temperature District Heating Networks." Proceedings 65, no. 1 (December 25, 2020): 25. http://dx.doi.org/10.3390/proceedings2020065008.

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District heating networks deliver around 13% of the heating energy in the EU, being considered as a key element of the progressive decarbonization of Europe. The H2020 REnewable Low TEmperature District project (RELaTED) seeks to contribute to the energy decarbonization of these infrastructures through the development and demonstration of the following concepts: reduction in network temperature down to 50 °C, integration of renewable energies and waste heat sources with a novel substation concept, and improvement on building-integrated solar thermal systems. The coupling of renewable thermal sources with ultra-low temperature district heating (DH) allows for a bidirectional energy flow, using the DH as both thermal storage in periods of production surplus and a back-up heating source during consumption peaks. The ultra-low temperature enables the integration of a wide range of energy sources such as waste heat from industry. Furthermore, RELaTED also develops concepts concerning district heating-connected reversible heat pump systems that allow to reach adequate thermal levels for domestic hot water as well as the use of the network for district cooling with high performance. These developments will be demonstrated in four locations: Estonia, Serbia, Denmark, and Spain.

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Ruesch, Florian, and Michel Haller. "Potential and limitations of using low-temperature district heating and cooling networks for direct cooling of buildings." Energy Procedia 122 (September 2017): 1099–104. http://dx.doi.org/10.1016/j.egypro.2017.07.443.

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Qi, Xianjun, Qinghui Chen, and Xiwei Zheng. "A short-term load forecasting taking into account the correlation of integrated energy load." E3S Web of Conferences 185 (2020): 01009. http://dx.doi.org/10.1051/e3sconf/202018501009.

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This paper proposes a short-term load forecasting method that takes into account the correlation of integrated energy load. The method use wavelet packet to decompose the electric cooling and heating load in frequency bands, analyze the cross-correlation of the electric cooling and heating load in each frequency band, and choose different forecasting methods according to the strength of the correlation to reflect the cross-correlation of the load itself; the method use recurrent neural network as a forecasting model to reflect the autocorrelation of the load itself. Compared with putting the electric cooling and heating load into the same recurrent neural network or back propagation neural network for forecasting, the method in this paper considers the autocorrelation of the electric cooling and heating load itself and the cross- correlation of the electric cooling and heating load in different frequency bands. This method reduces the average absolute percentage error of the load forecasting.

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Urbanucci, Testi, and Bruno. "Integration of Reversible Heat Pumps in Trigeneration Systems for Low-Temperature Renewable District Heating and Cooling Microgrids." Applied Sciences 9, no. 15 (August 5, 2019): 3194. http://dx.doi.org/10.3390/app9153194.

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District heating and cooling networks based on trigeneration systems and renewable energy technologies are widely acknowledged as an energy efficient and environmentally benign solution. These energy systems generally include back-up units, namely fossil-fuel boilers and electric chillers, to enhance system flexibility and cover peak energy demand. On the other hand, 4th generation district heating networks are characterized by low-temperature water distribution to improve energy and exergy efficiencies. Moreover, reversible heat pumps are a versatile technology, capable of providing both heating and cooling, alternately. In this paper, the integration of reversible heat pumps as single back-up units in hybrid renewable trigeneration systems serving low-energy micro-district heating and cooling networks is investigated. A detailed modeling of the system is provided, considering part-load and ambient condition effects on the performance of the units. Size and annual operation of the proposed system are optimized in a case study, namely a large office building located in Pisa (Italy), by means of a genetic algorithm-based procedure. A comparison with the conventional trigeneration system is performed in terms of economic and environmental perspectives. Results show that the integration of reversible heat pumps is an economically viable solution capable of reducing by 7% the equivalent annual cost, increasing the installed power of renewables up to 23%, and lowering by 11% carbon dioxide emissions, compared to the energy system with conventional back-up units.

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Moayedi, Hossein, and Amir Mosavi. "Double-Target Based Neural Networks in Predicting Energy Consumption in Residential Buildings." Energies 14, no. 5 (March 1, 2021): 1331. http://dx.doi.org/10.3390/en14051331.

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A reliable prediction of sustainable energy consumption is key for designing environmentally friendly buildings. In this study, three novel hybrid intelligent methods, namely the grasshopper optimization algorithm (GOA), wind-driven optimization (WDO), and biogeography-based optimization (BBO), are employed to optimize the multitarget prediction of heating loads (HLs) and cooling loads (CLs) in the heating, ventilation and air conditioning (HVAC) systems. Concerning the optimization of the applied algorithms, a series of swarm-based iterations are performed, and the best structure is proposed for each model. The GOA, WDO, and BBO algorithms are mixed with a class of feedforward artificial neural networks (ANNs), which is called a multi-layer perceptron (MLP) to predict the HL and CL. According to the sensitivity analysis, the WDO with swarm size = 500 proposes the most-fitted ANN. The proposed WDO-ANN provided an accurate prediction in terms of heating load (training (R2 correlation = 0.977 and RMSE error = 0.183) and testing (R2 correlation = 0.973 and RMSE error = 0.190)) and yielded the best-fitted prediction in terms of cooling load (training (R2 correlation = 0.99 and RMSE error = 0.147) and testing (R2 correlation = 0.99 and RMSE error = 0.148)).

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Jobard, Xavier, Pierryves Padey, Martin Guillaume, Alexis Duret, and Daniel Pahud. "Development and Testing of Novel Applications for Adsorption Heat Pumps and Chillers." Energies 13, no. 3 (February 1, 2020): 615. http://dx.doi.org/10.3390/en13030615.

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This work aims at the development and the experimental characterization of new applications for adsorption heat pumps and chillers driven by industrial waste heat or renewable sources that can provide heating and/or cooling. Adsorption technologies offer the advantage of providing heating and cooling from low temperature sources below 100 °C without using refrigerant with high Global Warming Potential and with very low electricity consumption. Therefore, the technology enables the use of large untapped heat sources, increasing the energy efficiency of the heating and cooling sector with very limited impact on the environment. Several applications were investigated numerically for Switzerland using a simplified model of an adsorption heat pump. Four scenarios were identified as interesting: (1) the valorization of low-grade industrial waste heat in district heating networks, (2) energy efficiency improvement of district heating substations, (3) an autonomous adsorption heat pump with a wood pellets burner and (4) cooling applications. These scenarios were experimentally validated with a laboratory test of a commercial silica gel/water machine. Results show that there is a gap of up to 40% between the prediction of the simplified model and the experimental results. Therefore, there is huge potential to improve the performances of this commercial unit for these applications.

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Sakawa, Masatoshi, and Takeshi Matsui. "Heat load prediction in district heating and cooling systems through recurrent neural networks." International Journal of Operational Research 23, no. 3 (2015): 284. http://dx.doi.org/10.1504/ijor.2015.069623.

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Arabkoohsar, A., and G. B. Andresen. "Supporting district heating and cooling networks with a bifunctional solar assisted absorption chiller." Energy Conversion and Management 148 (September 2017): 184–96. http://dx.doi.org/10.1016/j.enconman.2017.06.004.

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Meza-Cruz, Onesimo, Isaac Pilatowsky, Agustín Pérez-Ramírez, Carlos Rivera-Blanco, Youness El Hamzaoui, Miguel Perez-Ramirez, and Mauricio A. Sanchez. "Modeling a Thermochemical Reactor of a Solar Refrigerator by BaCl2-NH3 Sorption Using Artificial Neural Networks and Mathematical Symmetry Groups." Mathematical Problems in Engineering 2020 (September 30, 2020): 1–11. http://dx.doi.org/10.1155/2020/9098709.

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The aim of this work is to present a model for heat transfer, desorbed refrigerant, and pressure of an intermittent solar cooling system’s thermochemical reactor based on backpropagation neural networks and mathematical symmetry groups. In order to achieve this, a reactor was designed and built based on the reaction of BaCl2-NH3. Experimental data from this reactor were collected, where barium chloride was used as a solid absorbent and ammonia as a refrigerant. The neural network was trained using the Levenberg–Marquardt algorithm. The correlation coefficient between experimental data and data simulated by the neural network was r = 0.9957. In the neural network’s sensitivity analysis, it was found that the inputs, reactor’s heating temperature and sorption time, influence neural network’s learning by 35% and 20%, respectively. It was also found that, by applying permutations to experimental data and using multibase mathematical symmetry groups, the neural network training algorithm converges faster.

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Buffa, Simone, Anton Soppelsa, Mauro Pipiciello, Gregor Henze, and Roberto Fedrizzi. "Fifth-Generation District Heating and Cooling Substations: Demand Response with Artificial Neural Network-Based Model Predictive Control." Energies 13, no. 17 (August 21, 2020): 4339. http://dx.doi.org/10.3390/en13174339.

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District heating and cooling (DHC) is considered one of the most sustainable technologies to meet the heating and cooling demands of buildings in urban areas. The fifth-generation district heating and cooling (5GDHC) concept, often referred to as ambient loops, is a novel solution emerging in Europe and has become a widely discussed topic in current energy system research. 5GDHC systems operate at a temperature close to the ground and include electrically driven heat pumps and associated thermal energy storage in a building-sited energy transfer station (ETS) to satisfy user comfort. This work presents new strategies for improving the operation of these energy transfer stations by means of a model predictive control (MPC) method based on recurrent artificial neural networks. The results show that, under simple time-of-use utility rates, the advanced controller outperforms a rule-based controller for smart charging of the domestic hot water (DHW) thermal energy storage under specific boundary conditions. By exploiting the available thermal energy storage capacity, the MPC controller is capable of shifting up to 14% of the electricity consumption of the ETS from on-peak to off-peak hours. Therefore, the advanced control implemented in 5GDHC networks promotes coupling between the thermal and the electric sector, producing flexibility on the electric grid.

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Atienza-Márquez, Antonio, Joan Carles Bruno, and Alberto Coronas. "Recovery and Transport of Industrial Waste Heat for Their Use in Urban District Heating and Cooling Networks Using Absorption Systems." Applied Sciences 10, no. 1 (December 31, 2019): 291. http://dx.doi.org/10.3390/app10010291.

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The use of industrial excess heat in district heating networks is very attractive. The main issue is the transport of the heat from the point of generation to the local distribution network, in a way similar to the structure of electricity transport and distribution networks. Absorption systems have been proposed to transport and distribute waste heat using two absorption stations. In one of them (step-up station), industrial heat at a low temperature is pumped to a higher temperature to facilitate its transport and at the same time increase the temperature difference between the supply and return streams, in this way reducing the hot water mass flow rate circulating through the heat transport network. Heat is then used in a second absorption system (step-down station) to provide heat to a low temperature local district network. In this paper, several absorption system configurations are analyzed for both stations. A detailed thermodynamic analysis of each configuration is performed using selected energy performance indicators to calculate its global performance. The implementation of these kind of systems could enable the use of waste heat to produce heating and cooling for smart communities located a few dozens of kilometers away from industrial sites.

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Suzuki, Ikuo, Masaru Fujii, Keitaro Naruse, Hiroshi Yokoi, and Yukinori Kakazu. "Acquisition of Adaptive Behavior for the SMA-Net Robot Using Chaotic Neural Networks." Journal of Robotics and Mechatronics 16, no. 4 (August 20, 2004): 411–19. http://dx.doi.org/10.20965/jrm.2004.p0411.

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In this paper, we propose control for the SMA-Net Robot, a flexible structure consisting of many units and shape memory alloy (SMA) springs. To generate greater force for movement, more than one SMA spring is required. Since SMA springs are driven by thermal transition, controlling individual spring heating patterns is important in SMA-Net Robot behavior. It is a problem in controlling SMA spring that its detailed control is difficult because of the nonlinearity. We propose methodology that arranges heating and cooling as a rhythm pattern memorized by many chaotic neural networks (CNNs). To renew connecting weights in the network, we use the modified dynamic learning method (DLM) in online learning. The results of computational experiments showed that the SMA-Net Robot with the proposed control generates movement automatically.

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Sameti, Mohammad, and Fariborz Haghighat. "Optimization approaches in district heating and cooling thermal network." Energy and Buildings 140 (April 2017): 121–30. http://dx.doi.org/10.1016/j.enbuild.2017.01.062.

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von Rhein, Justus, Gregor P. Henze, Nicholas Long, and Yangyang Fu. "Development of a topology analysis tool for fifth-generation district heating and cooling networks." Energy Conversion and Management 196 (September 2019): 705–16. http://dx.doi.org/10.1016/j.enconman.2019.05.066.

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Ahmadisedigh, Hossein, and Louis Gosselin. "Combined heating and cooling networks with waste heat recovery based on energy hub concept." Applied Energy 253 (November 2019): 113495. http://dx.doi.org/10.1016/j.apenergy.2019.113495.

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Clarke, Fiona, Bogdan Dorneanu, Evgenia Mechleri, and Harvey Arellano-Garcia. "Optimal design of heating and cooling pipeline networks for residential distributed energy resource systems." Energy 235 (November 2021): 121430. http://dx.doi.org/10.1016/j.energy.2021.121430.

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Casisi, Melchiorre, Dario Buoro, Piero Pinamonti, and Mauro Reini. "A Comparison of Different District Integration for a Distributed Generation System for Heating and Cooling in an Urban Area." Applied Sciences 9, no. 17 (August 27, 2019): 3521. http://dx.doi.org/10.3390/app9173521.

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The paper proposes a comparison of different district integration options for a distributed generation system for heating and cooling in an urban area. The system considered includes several production units located close to the users, a central unit and the district heating and cooling network which can connect all the users to each other and to a central unit, where a cogeneration system and a solar plant can be placed. Thus, each user can be regarded as isolated from the others, satisfying its energy needs by means of an autonomous production unit. Alternatively, it can be connected to the others through the district heating and cooling network. When a district heating and cooling network is included in the design option the synthesis-design and operation problems cannot be solved separately, because the energy to be produced by each production site is not known in advance, as the flows through the district heating and cooling network are not defined. This paper uses a mixed integer linear programming (MILP) methodology for the multi-objective optimization of the distributed generation energy system, considering the total annual cost for owning, operating and maintaining the whole system as the economic objective function, while the total annual CO2 emissions as the environmental objective function. The energy system is optimized for different district integration option, in order to understand how they affect the optimal solutions compared with both the environmental and economic objects.

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Fan, Qingwu, Li Shuo, and Xudong Liu. "Prediction of Building Energy Consumption Based on IPSO-CLSTM Neural Network." Journal of Autonomous Intelligence 3, no. 2 (June 28, 2021): 11. http://dx.doi.org/10.32629/jai.v3i2.285.

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Accurate prediction of building load is essential for energy saving and environmental protection. Exploring the impact of building characteristics on heating and cooling load can improve energy efficiency from the design stage of the building. In this paper, a prediction model of building heating and cooling loads is proposed, which based on Improved Particle Swarm Optimization (IPSO) algorithm and Convolution Long Short-Term Memory (CLSTM) neural network model. Firstly, the characteristic variables are extracted and evaluated by Spearman’s correlation coefficient method; Then the prediction model based on the CLSTM neural network is constructed to predict building heating and cooling load. The IPSO algorithm is adopted to solve the problem that manual work cannot precisely adjust parameters. In this method, the optimization ability of the PSO algorithm is improved by changing the updating rule of inertia weight and learning factors. Finally, the parameters of the neural network are taken as IPSO optimization object to improve the prediction accuracy. In the experimental stage of this paper, a variety of algorithm models are compared, and the results show that IPSO-CLSTM can get the best results in the prediction of heating and cooling load.

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Huttunen, Jari, Olli Salmela, Topi Volkov, and Eva Pongrácz. "Reducing the Cooling Energy Consumption of Telecom Sites by Liquid Cooling." Proceedings 58, no. 1 (September 11, 2020): 19. http://dx.doi.org/10.3390/wef-06908.

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The use of mobile data has increased and will continue to increase in the future, because more data is moving to wireless networks such as 5G. Cooling energy need is also expected to increase in indoor telecom rooms, and can be as high as the equipment’s own power consumption. The world’s first liquid Base Transceiver Station (BTS) was adopted into commercial use in 2018, in Helsinki, Finland. Conventional air-cooled BTS hardware was converted into liquid-cooled BTS equipment. Heat from the BTS was pumped out of the site room, and thus ventilation or air conditioning was not needed for the heat load from the BTS. Heat stored in the liquid was released into the ventilation duct of the building, still providing annual cooling energy savings of 70%, when compared to air cooling. In the future, 80% of the total dissipated energy, 13450 kWh/a in total, can potentially be used for heating purposes. In terms of CO2 emissions, adapting liquid cooling showed an 80% reduction potential when compared to air cooling.

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Boesten, Stef, Wilfried Ivens, Stefan C. Dekker, and Herman Eijdems. "5th generation district heating and cooling systems as a solution for renewable urban thermal energy supply." Advances in Geosciences 49 (September 20, 2019): 129–36. http://dx.doi.org/10.5194/adgeo-49-129-2019.

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Abstract. In order to reduce greenhouse gas emissions and decrease dependency on depleting fossil fuel resources the shift to a renewable energy system is necessary. District heating and cooling systems are a viable solution to provide heat and cold in urban environments. Renewable heat and cold sources that may get incorporated in future urban energy systems will not provide the same high temperature output as current fossil fuel fired systems. Fifth generation district heating and cooling (5GDHC) systems are decentralized, bi-directional, close to ground temperature networks that use direct exchange of warm and cold return flows and thermal storage to balance thermal demand as much as possible. 5GDHC offers a way to incorporate low temperature renewable heat sources including shallow geothermal energy, as well as reduce total demand by recuperating generated heat from cooling and generated cold from heating. The large scale of 5GDHC allows for optimal design of technical parts like heat pumps and thermal storage vessels, while increasing overall system efficiency by incorporating a large variety of supply and demand profiles. We provide a definition for 5GDHC and show how this concept differs from conventional district heating systems. The Mijnwater system in Heerlen, the Netherlands is showing what a city-level 5GDHC system can look like.

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Aleksahin, A., A. Boblovskii, K. Miahkokhlib, and Y. Schactnyi. "INDICATORS THERMAL AND HYDRAULIC MODES OF DISTRIBUTION HEAT NETWORKS." Municipal economy of cities 3, no. 163 (June 29, 2021): 7–11. http://dx.doi.org/10.33042/2522-1809-2021-3-163-7-11.

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The task of heat supply systems is to ensure the quality of services at a minimum cost in the production and transportation of thermal energy. Because the distribution heat networks of district and district systems are characterized by significant branching and significantly longer lengths of heat pipelines compared to the main sections of heating networks, reducing heat loss in these elements of the heating system significantly affects the overall efficiency of district heating. The amount of heat loss depends on the method of laying networks and the diameter of heat pipes, thermal insulation parameters and temperature of the coolant and the environment. Based on the formulas for determining the specific pressure losses to overcome the friction forces obtained from the generalization of these projects of central heating systems of a number of residential districts of Kharkiv, calculated dependences for determining heat losses by supply and return pipelines of the main branches of the heating network. Estimates of accuracy of use of the offered formulas are carried out. The aim of the work is to determine the specific pressure losses for the main branches of heating distribution networks on the basis of generalization of design data for heat supply systems of building groups and clarification of formulas for calculating heat losses by pipelines of central heating distribution system. Based on the generalization of projects for the heating network of residential neighborhoods in Kharkiv, a formula for calculating the specific pressure loss during water movement in the pipelines of the main branches of the heating network depending on the heat load of buildings connected to the branch. Formulas for calculation of heat losses by supply and return pipelines of the main branches of a heating network of the residential district are offered. A comparison of the accuracy of calculations using the proposed formulas with existing methods for determining heat loss in branched heat supply networks, which showed the possibility of using formulas in preliminary assessments of the thermal state of networks.

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Sholahudin, Azimil Gani Alam, Chang In Baek, and Hwataik Han. "Prediction and Analysis of Building Energy Efficiency Using Artificial Neural Network and Design of Experiments." Applied Mechanics and Materials 819 (January 2016): 541–45. http://dx.doi.org/10.4028/www.scientific.net/amm.819.541.

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Energy consumption of buildings is increasing steadily and occupying approximately 30-40% of total energy use. It is important to predict heating and cooling loads of a building in the initial stage of design to find out optimal solutions among various design options, as well as in the operating stage after the building has been completed for energy efficient operation. In this paper, an artificial neural network model has been developed to predict heating and cooling loads of a building based on simulation data for building energy performance. The input variables include relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution of a building, and the output variables include heating load (HL) and cooling load (CL) of the building. The simulation data used for training are the data published in the literature for various 768 residential buildings. ANNs have a merit in estimating output values for given input values satisfactorily, but it has a limitation in acquiring the effects of input variables individually. In order to analyze the effects of the variables, we used a method for design of experiment and conducted ANOVA analysis. The sensitivities of individual variables have been investigated and the most energy efficient solution has been estimated under given conditions. Discussions are included in the paper regarding the variables affecting heating load and cooling load significantly and the effects on heating and cooling loads of residential buildings.

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Neirotti, Francesco, Michel Noussan, Stefano Riverso, and Giorgio Manganini. "Analysis of Different Strategies for Lowering the Operation Temperature in Existing District Heating Networks." Energies 12, no. 2 (January 21, 2019): 321. http://dx.doi.org/10.3390/en12020321.

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District heating systems have an important role in increasing the efficiency of the heating and cooling sector, especially when coupled to combined heat and power plants. However, in the transition towards decarbonization, current systems show some challenges for the integration of Renewable Energy Sources and Waste Heat. In particular, a crucial aspect is represented by the operating temperatures of the network. This paper analyzes two different approaches for the decrease of operation temperatures of existing networks, which are often supplying old buildings with a low degree of insulation. A simulation model was applied to some case studies to evaluate how a low-temperature operation of an existing district heating system performs compared to the standard operation, by considering two different approaches: (1) a different control strategy involving nighttime operation to avoid the morning peak demand; and (2) the partial insulation of the buildings to decrease operation temperatures without the need of modifying the heating system of the users. Different temperatures were considered to evaluate a threshold based on the characteristics of the buildings supplied by the network. The results highlight an interesting potential for optimization of existing systems by tuning the control strategies and performing some energy efficiency operation. The network temperature can be decreased with a continuous operation of the system, or with energy efficiency intervention in buildings, and distributed heat pumps used as integration could provide significant advantages. Each solution has its own limitations and critical parameters, which are discussed in detail.

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Rong, Haina, and Francisco de León. "Load Estimation of Complex Power Networks from Transformer Measurements and Forecasted Loads." Complexity 2020 (January 22, 2020): 1–14. http://dx.doi.org/10.1155/2020/2941809.

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This paper presents a load estimation method applicable to complex power networks (namely, heavily meshed secondary networks) based on available network transformer measurements. The method consists of three steps: network reduction, load forecasting, and state estimation. The network is first mathematically reduced to the terminals of loads and measurement points. A load forecasting approach based on temperature is proposed to solve the network unobservability. The relationship between outdoor temperature and power consumption is studied. A power-temperature curve, a nonlinear function, is obtained to forecast loads as the temperature varies. An “effective temperature” reflecting complex weather conditions (sun irradiation, humidity, rain, etc.) is introduced to properly consider the effect on the power consumption of cooling and heating devices. State estimation is adopted to compute loads using network transformer measurements and forecasted loads. Experiments conducted on a real secondary network in New York City with 1040 buses verify the effectiveness of the proposed method.

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Neirotti, Francesco, Michel Noussan, and Marco Simonetti. "Evaluating the Emissions of the Heat Supplied by District Heating Networks through A Life Cycle Perspective." Clean Technologies 2, no. 4 (October 6, 2020): 392–405. http://dx.doi.org/10.3390/cleantechnol2040024.

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The Life Cycle Assessment methodology has proven to be effective in evaluating the impacts of goods production throughout their life cycle. While many studies are available on specific products, in recent years a growing interest is related to the analysis of services, including energy supply for final customers. Different LCA evaluations are available for electricity, while the heating and cooling sector has not yet been properly investigated. The objective of this study is the analysis of the specific impacts of the heat supplied to the final users connected to a district heating system, in comparison with traditional individual natural gas boilers, which represent the baseline heating solution in several urban contexts in Europe. The results show that the comparison is heavily dependent on the allocation method used for combined heat and power plant production. District Heating impact on heat supplied to the users can vary from 0.10 to 0.47 kgCO2eq/kWh, while distributed natural gas boilers present an overall impact equal to 0.27 kgCO2eq/kWh.

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Simonsson, Johan, Khalid Tourkey Atta, Gerald Schweiger, and Wolfgang Birk. "Experiences from City-Scale Simulation of Thermal Grids." Resources 10, no. 2 (January 25, 2021): 10. http://dx.doi.org/10.3390/resources10020010.

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Dynamic simulation of district heating and cooling networks has an increased importance in the transition towards renewable energy sources and lower temperature district heating grids, as both temporal and spatial behavior need to be considered. Even though much research and development has been performed in the field, there are several pitfalls and challenges towards dynamic district heating and cooling simulation for everyday use. This article presents the experiences from developing and working with a city-scale simulator of a district heating grid located in Luleå, Sweden. The grid model in the case study is a physics based white-box model, while consumer models are either data-driven black-box or gray-box models. The control system and operator models replicate the manual and automatic operation of the combined heat and power plant. Using the functional mock-up interface standard, a co-simulation environment integrates all the models. Further, the validation of the simulator is discussed. Lessons learned from the project are presented along with future research directions, corresponding to identified gaps and challenges.

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Hałaj, Elżbieta, Jarosław Kotyza, Marek Hajto, Grzegorz Pełka, Wojciech Luboń, and Paweł Jastrzębski. "Upgrading a District Heating System by Means of the Integration of Modular Heat Pumps, Geothermal Waters, and PVs for Resilient and Sustainable Urban Energy." Energies 14, no. 9 (April 21, 2021): 2347. http://dx.doi.org/10.3390/en14092347.

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Krakow has an extensive district heating network, which is approximately 900 km long. It is the second largest city in terms of the number of inhabitants in Poland, resulting in a high demand for energy—for both heating and cooling. The district heating of the city is based on coal. The paper presents the conception of using the available renewable sources to integrate them into the city’s heating system, increasing the flexibility of the system and its decentralization. An innovative solution of the use of hybrid, modular heat pumps with power dependent on the needs of customers in a given location and combining them with geothermal waters and photovoltaics is presented. The potential of deep geothermal waters is based on two reservoirs built of carbonate rocks, namely Devonian and Upper Jurassic, which mainly consist of dolomite and limestone. The theoretical potential of water intake equal to the nominal heating capacity of a geothermal installation is estimated at 3.3 and 2.0 MW, respectively. Shallow geothermal energy potential varies within the city, reflecting the complex geological structure of the city. Apart from typical borehole heat exchangers (BHEs), the shallower water levels may represent a significant potential source for both heating and cooling by means of water heat pumps. For the heating network, it has been proposed to use modular heat pumps with hybrid sources, which will allow for the flexible development of the network in places previously unavailable or unprofitable. In the case of balancing production and demand, a photovoltaic installation can be an effective and sufficient source of electricity that will cover the annual electricity demand generated by the heat pump installation, when it is used for both heating and cooling. The alternating demand of facilities for heating and cooling energy, caused by changes in the seasons, suggests potential for using seasonal cold and heat storage.

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Tien Bui, Dieu, Hossein Moayedi, Dounis Anastasios, and Loke Kok Foong. "Predicting Heating and Cooling Loads in Energy-Efficient Buildings Using Two Hybrid Intelligent Models." Applied Sciences 9, no. 17 (August 29, 2019): 3543. http://dx.doi.org/10.3390/app9173543.

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Today, energy conservation is more and more stressed as great amounts of energy are being consumed for varying applications. This study aimed to evaluate the application of two robust evolutionary algorithms, namely genetic algorithm (GA) and imperialist competition algorithm (ICA) for optimizing the weights and biases of the artificial neural network (ANN) in the estimation of heating load (HL) and cooling load (CL) of the energy-efficient residential buildings. To this end, a proper dataset was provided composed of relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, glazing area distribution, as the HL and CL influential factors. The optimal structure of each model was achieved through a trial and error process and to evaluate the accuracy of the designed networks, we used three well-known accuracy criterions. As the result of applying GA and ICA, the performance error of ANN decreased respectively by 17.92% and 23.22% for the HL, and 21.13% and 24.53% for CL in the training phase, and 20.84% and 23.74% for HL, and 27.57% and 29.10% for CL in the testing phase. The mentioned results demonstrate the superiority of the ICA-ANN model compared to GA-ANN and ANN.

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Chinese, Damiana. "Optimal size and layout planning for district heating and cooling networks with distributed generation options." International Journal of Energy Sector Management 2, no. 3 (September 12, 2008): 385–419. http://dx.doi.org/10.1108/17506220810892946.

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Journal articles on the topic 'Heating and cooling networks'

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