Relevant bibliographies by topics / District heating load prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'District heating load prediction'

Author: Grafiati
Published: 5 June 2025
Last updated: 16 July 2025

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Journal articles on the topic "District heating load prediction"

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Wang, Tao, Tingyu Ma, Dongsong Yan, et al. "Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine." Thermal Science, no. 00 (2020): 307. http://dx.doi.org/10.2298/tsci200529307w.

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District heating systems are an important part of the future smart energy system and are seen as a tool to achieve energy efficiency goals in the EU. In order to achieve the real sense of heating on demand, based on historical heating load data, first of all, the heating load time series data was dealing with fuzzy information granulation, and then the cross-validation was used to explore the advantages of the data potential. Then the support vector machine regression prediction model was used for the prediction of the granulation data, finally, the heating load of a district heating system is simulated and verified. The simulation results show that the prediction model can effectively predict the trend of heating load, and provide a theoretical basis for the prediction of district heating load.
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Guelpa, Elisa, Ludovica Marincioni, Martina Capone, Stefania Deputato, and Vittorio Verda. "Thermal load prediction in district heating systems." Energy 176 (June 2019): 693–703. http://dx.doi.org/10.1016/j.energy.2019.04.021.

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Xue, Guixiang, Yu Pan, Tao Lin, Jiancai Song, Chengying Qi, and Zhipan Wang. "District Heating Load Prediction Algorithm Based on Feature Fusion LSTM Model." Energies 12, no. 11 (2019): 2122. http://dx.doi.org/10.3390/en12112122.

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The smart district heating system (SDHS) is an important element of the construction of smart cities in Northern China; it plays a significant role in meeting heating requirements and green energy saving in winter. Various Internet of Things (IoT) sensors and wireless transmission technologies are applied to monitor data in real-time and to form a historical database. The accurate prediction of heating loads based on massive historical datasets is the necessary condition and key basis for formulating an optimal heating control strategy in the SDHS, which contributes to the reduction in the consumption of energy and the improvement in the energy dispatching efficiency and accuracy. In order to achieve the high prediction accuracy of SDHS and to improve the representation ability of multi-time-scale features, a novel short-term heating load prediction algorithm based on a feature fusion long short-term memory (LSTM) model (FFLSTM) is proposed. Three characteristics, namely proximity, periodicity, and trend, are found after analyzing the heating load data from the aspect of the hourly time dimension. In order to comprehensively utilize the data’s intrinsic characteristics, three LSTM models are employed to make separate predictions, and, then, the prediction results based on internal features and other external features at the corresponding moments are imported into the high-level LSTM model for fusion processing, which brings a more accurate prediction result of the heating load. Detailed comparisons between the proposed FFLSTM algorithm and the-state-of-art algorithms are conducted in this paper. The experimental results show that the proposed FFLSTM algorithm outperforms others and can obtain a higher prediction accuracy. Furthermore, the impact of selecting different parameters of the FFLSTM model is also studied thoroughly.
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Lavikainen, Vili, and Pasi Fränti. "Clustering district heating customers based on load profiles." Applied Computing and Intelligence 4, no. 2 (2024): 269–81. https://doi.org/10.3934/aci.2024016.

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<p>Intelligent district heating control requires knowing the customers' past behavior and predicting their future needs. This can reduce peak energy use, optimizing energy production, accurate billing, and reducing fraud. Clustering has been used for analyzing large-scale building operational data and recognizing consumption profiles. In this work, we analyze the heat consumption profiles of district heat customers in Kuopio, Finland. We constructed two consumption profiles of their average hourly use: one for weekdays, and one for weekends. Clustering is then used to construct four consumption profiles. These profiles can be used for intelligent control, prediction of future use, and to recognize abnormal use behavior. The latter can be the first indication of a problem like heat leaking, which can prevent possible water damage.</p>
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Rusovs, D., L. Jakovleva, V. Zentins, and K. Baltputnis. "Heat Load Numerical Prediction for District Heating System Operational Control." Latvian Journal of Physics and Technical Sciences 58, no. 3 (2021): 121–36. http://dx.doi.org/10.2478/lpts-2021-0021.

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Abstract To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error.
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Simonovic, Milos, Vlastimir Nikolic, Emina Petrovic, and Ivan Ciric. "Heat load prediction of small district heating system using artificial neural networks." Thermal Science 20, suppl. 5 (2016): 1355–65. http://dx.doi.org/10.2298/tsci16s5355s.

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Accurate models for heat load prediction are essential to the operation and planning of a utility company. Load prediction helps a heat utility to make important and advanced decisions in district heating systems. As a popular data driven method, artificial neural networks are often used for prediction. The main idea is to achieve quality prediction for a short period in order to reduce the consumption of heat energy production and increased coefficient of exploitation of equipment. To improve the short term prediction accuracy, this paper presents a kind of improved artificial neural network model for 1 to 7 days ahead prediction of heat consumption of energy produced in small district heating system. Historical data set of one small district heating system from city of Nis, Serbia, was used. Particle swarm optimization is applied to adjust artificial neural network weights and threshold values. In this paper, application of feed forward artificial neural network for short-term prediction for period of 1, 3, and 7 days, of small district heating system, is presented. Two test data sets were considered with different interruption non-stationary performances. Comparison of prediction accuracy between regular and improved artificial neural network model was done. The comparison results reveal that improved artificial neural network model have better accuracy than that of artificial neural network ones.
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Liu, Lan Bin, Ai Juan Zou, and Yu Fei Ma. "A Method of Load Prediction in District-Heating System Based on Data Mining." Advanced Materials Research 918 (April 2014): 154–59. http://dx.doi.org/10.4028/www.scientific.net/amr.918.154.

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According to the internal mechanism of the formation of heat load, the formation of heat load consists of two parts, the systemic heat load, which is determined by the building envelope and outdoor environmental parameters and random load caused by the users randomness of events and solar radiation etc. Toward systemic heat load, this paper considered the influence of environmental parameters before the prediction time and used the method of stepwise trials and MSE to obtain the optimal solution. Toward random load, it is considered that the day of the same type have the same variation pattern. On this basis, this paper introduced a correction coefficient to obtain random load eventually. This paper selected DeST, the widely used energy simulation software in China, to analysis the case. The result shows that the prediction method is feasible and 50% of the predicted loads have the relative error of less than 5%.
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Lyu, Yan, Yiqun Pan, and Zhizhong Huang. "Development and Test of a New Fast Estimate Tool for Cooling and Heating Load Prediction of District Energy Systems at Planning Stage." Buildings 12, no. 10 (2022): 1671. http://dx.doi.org/10.3390/buildings12101671.

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During the design and planning stage of a district energy system, the prediction of the cooling and heating loads is an important step. The accurate estimate of the load pattern can provide a basis for the configuration and optimization of the system. To meet the demand in practical application, this paper proposes a fast load prediction method for district energy systems based on a presimulated forward modelling database and KNN (K-nearest neighbor) algorithm and develops it into a practical tool. Owing to the absence of some design parameters at the planning stage, scenario analysis is also used to determine some input conditions for load prediction. In this paper, the scenarios cover three types of building: office, shopping mall and hotel. To test the performance of this new method, we randomly selected 15 virtual buildings (5 buildings for each type) with different design parameters and took their detailed BPS (building performance simulation) model as a benchmark to assess the prediction results of the new method. The index “ratio of the hours with effective prediction” is defined as the ratio of the hours whose relative error of hourly load prediction is less than 15% to the hours whose load is not 0 in the whole year, and the test result shows that this index is not less than 0.9 (90%) for the predicted cooling load of all 45 test cases and the predicted heating load of 25 of the 45 cases. As a research achievement with practical value, this paper accomplishes the programming work of the tool and makes it into a software. The application of this software in the actual project of district energy system is also presented. The performance of the new load prediction tool was compared with the traditional approach commonly used in engineering—the load estimation based on reference building models—and the result shows that the fast load estimate tool can provide the same level of prediction accuracy as traditional simulation methods.
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Gong, M., C. Han, J. Sun, Y. Zhao, S. Li, and W. Xu. "Heat Load Prediction of District Heating Systems Based on SCSO-TCN." Thermal Engineering 71, no. 4 (2024): 358–63. http://dx.doi.org/10.1134/s0040601524040013.

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Dalipi, Fisnik, Sule Yildirim Yayilgan, and Alemayehu Gebremedhin. "Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study." Applied Computational Intelligence and Soft Computing 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3403150.

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We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS). Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific. For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model. The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF). We use the data collected from buildings at several locations for a period of 29 weeks. Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient. In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms. The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.
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Dissertations / Theses on the topic "District heating load prediction"

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Bergentz, Tobias. "Identifying symptoms of fault in District Heating Substations : An investigation in how a predictive heat load software can help with fault detection." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-174442.

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District heating delivers more than 70% of the energy used for heating and domestichot water in Swedish buildings. To stay competitive, district heating needs toreduce its losses and increase capabilities to utilise low grade heat. Finding faultysubstations is one way to allow reductions in supply temperatures in district heatingnetworks, which in turn can help reduce the losses. In this work three suggestedsymptoms of faults: abnormal quantization, drifting and anomalous values, are investigatedwith the help of hourly meter data of: heat load, volume flow, supplyand return temperatures from district heating substations. To identify abnormalquantization, a method is proposed based on Shannon’s entropy, where lower entropysuggests higher risk of abnormal quantization. The majority of the substationsidentified as having abnormal quantization with the proposed method has a meterresolution lower than the majority of the substations in the investigated districtheating network. This lower resolution is likely responsible for identifying thesesubstation, suggesting the method is limited by the meter resolution of the availabledata. To improve result from the method higher resolution and sampling frequencyis likely needed.For identifying drift and anomalous values two methods are proposed, one for eachsymptom. Both methods utilize a software for predicting hourly heat load, volumeflow, supply and return temperatures in individual district heating substations.The method suggested for identifying drift uses the mean value of each predictedand measured quantity during the investigated period. The mean of the prediction iscompared to the mean of the measured values and a large difference would suggestrisk of drift. However this method has not been evaluated due to difficulties infinding a suitable validation method.The proposed method for detecting anomalous values is based on finding anomalousresiduals when comparing the prediction from the prediction software to themeasured values. To find the anomalous residuals the method uses an anomalydetection algorithm called IsolationForest. The method produces rankable lists inwhich substations with risk of anomalies are ranked higher in the lists. Four differentlists where evaluated by an experts. For the two best preforming lists approximatelyhalf of the top 15 substations where classified to contain anomalies by the expertgroup. The proposed method for detecting anomalous values shows promising resultespecially considering how easily the method could be added to a district heatingnetwork. Future work will focus on reducing the number of false positives. Suggestionsfor lowering the false positive rate include, alternations or checks on theprediction models used.
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Rodriguez, German Darío Rivas. "Decentralized Architecture for Load Balancing in District Heating Systems." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3329.

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Context. In forthcoming years, sustainability will lead the development of society. Implementation of innovative systems to make the world more sustainable is becoming one of the key points for science. Load balancing strategies aim to reduce economic and ecological cost of the heat production in district heating systems. Development of a decentralized solution lies in the objective of making the load balancing more accessible and attractive for the companies in charge of providing district-heating services. Objectives. This master thesis aims to find a new alternative for implementing decentralized load balancing in district heating systems. Methods. The development of this master thesis involved the review of the state-of-the-art on demand side management in district heating systems and power networks. It also implied the design of the architecture, creation of a software prototype and execution of a simulation of the system to measure the performance in terms of response time. Results. Decentralized demand side management algorithm and communication framework, software architecture description and analysis of the prototype simulation performance. Conclusions. The main conclusion is that it is possible to create a decentralized algorithm that performs load balancing without compromising the individuals’ privacy. It is possible to say that the algorithm shows good levels of performance not only from the system aggregated response time, but also from the individual performance, in terms of memory consumption and CPU consumption.<br>(+46) 709706206
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Sultan, Sahira. "Cost Evaluation of Building Space Heating; District Heating and Heat Pumps." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-37137.

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Climate change and energy efficiency has become a matter of concern in recent times; therefore, energy efficiency of buildings has drawn major attention. According to the European Commission, EU countries must improve energy efficiency of existing buildings by retrofitting and renovating the buildings. A case study of a renovated commercial building is considered in this degree project. A model of the building is developed in the IDA Indoor Climate and Energy (IDA ICE) software. The model is then augmented to include renovations in the building. Further, the model is simulated in IDA ICE before and after renovations to investigate the impact of renovations on energy consumption of the building for one year. The simulation results indicate peak demands of district heating that occur in the coldest days of the year. The peak demands of energy are expected to increase the district heating cost because they serve as a basis for new pricing model introduced by the energy providers. Hence, it is important from the customer point of view to reduce the peak loads for cost shavings. The project work also provides an insight into the alternative source of energy such as heat pumps to reduce the peak load demands of district heating.
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Provatas, Spyridon. "An Online Machine Learning Algorithm for Heat Load Forecasting in District Heating Systems." Thesis, Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3475.

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Context. Heat load forecasting is an important part of district heating optimization. In particular, energy companies aim at minimizing peak boiler usage, optimizing combined heat and power generation and planning base production. To achieve resource efficiency, the energy companies need to estimate how much energy is required to satisfy the market demand. Objectives. We suggest an online machine learning algorithm for heat load forecasting. Online algorithms are increasingly used due to their computational efficiency and their ability to handle changes of the predictive target variable over time. We extend the implementation of online bagging to make it compatible to regression problems and we use the Fast Incremental Model Trees with Drift Detection (FIMT-DD) as the base model. Finally, we implement and incorporate to the algorithm a mechanism that handles missing values, measurement errors and outliers. Methods. To conduct our experiments, we use two machine learning software applications, namely Waikato Environment for Knowledge Analysis (WEKA) and Massive Online Analysis (MOA). The predictive ability of the suggested algorithm is evaluated on operational data from a part of the Karlshamn District Heating network. We investigate two approaches for aggregating the data from the nodes of the network. The algorithm is evaluated on 100 runs using the repeated measures experimental design. A paired T-test is run to test the hypothesis that the the choice of approach does not have a significant effect on the predictive error of the algorithm. Results. The presented algorithm forecasts the heat load with a mean absolute percentage error of 4.77\%. This means that there is a sufficiently accurate estimation of the actual values of the heat load, which can enable heat suppliers to plan and manage more effectively the heat production. Conclusions. Experimental results show that the presented algorithm can be a viable alternative to state-of-the-art algorithms that are used for heat load forecasting. In addition to its predictive ability, it is memory-efficient and can process data in real time. Robust heat load forecasting is an important part of increased system efficiency within district heating, and the presented algorithm provides a concrete foundation for operational usage of online machine learning algorithms within the domain.
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Poutiainen, Zacharias. "Short-Term Heat Load Forecasting in District Heating Systems : A Comparative Study of Various Forecasting Methods." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-265670.

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Short term heat load forecasts are vital for optimal production planning and commitment of generation units. The generation utility also bares a balance responsibility toward the electricity market as a result of CHP generation. Sub-optimal load forecasts can lead to high costs relating to unit commitment, fuel usage and balancing costs. This thesis presents the empirical comparison of various models for 24h heat load forecasting. Five methods were investigated including four supervised machine learning algorithms; neural networks, support vector machines, random forests and boosted decision trees and one auto-regressive time series model; ARIMAX. The models were developed, and evaluated using cross validation with one year of hourly heat load data from a local district heating system and corresponding meteorological data from the same time period. The thesis also investigates the impact of feature selection on the predictive power and generalization ability of the models. The results indicate a significant difference in forecast accuracy between the methods with neural networks and ARIMAX showing the best and similar performance followed by the support vector machine, boosted decision trees and random forest.<br>Korttidsprognoser för fjärrvärmelast är mycket viktiga för optimal produktionsplanering. Energibolag som använder kraftvärme bär dessutom balansansvar gentemot elmarknaden. Sub-optimala lastprognoser kan leda till höga kostnader för start och stopp, bränsleåtgång och obalanser. Detta examensarbete presenterar den empiriska jämförelsen av olika modeller avseende 24-timmars lastprognostisering. Totalt fem metoder undersöktes varav fyra maskininlärningsalgoritmer; neurala nätverk, stödvektormaskin, random forest samt boosted desicion trees och en tidsseriemodell; ARIMAX. Modellerna utvecklades, och utvärderades med hjälp av korsvalidering på ett års värden av timvis lastdata från ett lokalt fjärrvärmenät och motsvarande väderdata för samma tidsperiod. Examensarbetet undersöker även inverkan av variabelselektion på prognosernas precision och förmåga att generalisera. Resultaten tyder på en signifikant skillnad i noggrannhet mellan de olika modellerna. Bäst resultat uppnåddes av neurala nätverk och ARIMAX med en liten skillnad sinsemellan, följt av stödvektormaskin, boosted decision trees och random forest.
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Eriksson, Andreas. "Metoder för lastprioritering i fjärrvärmecentraler." Thesis, Uppsala universitet, Fasta tillståndets fysik, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-139466.

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A problem in the field of district heating is the oil burners needed to provide power when a peak load occurs. One possible way of reducing the needed amount of oil is to reduce the power demand for space heating in the district-heating substations when the need for district heating water exceeds a certain limit. This can be done by use of a locally working controller function. In this Thesis the options concerning load priority are evaluated. To evaluate the potential for using controller functions concerning peak load priority an experiment was brought out in a chosen district-heating substation. The impact on the indoor thermal comfort during a heat reduction was also taken into account. With simulations and mathematical models the building and the indoor air cool down was evaluated. Also a survey was given to the residents to validate how the indoor thermal climate was affected during the experiment. Possible savings by using these kinds of functions were also accounted for. The result demonstrates that a simple controller function provides a possible way of reducing the power demand, but is not sufficiently reliable. This is mainly due to the used regulating parameter. With modifications or by adding additional regulating parameters such as water flow into the controller, the functionality can be improved. The result from the survey shows that during the experimental period the residents experienced a minor impact on the thermal comfort. Parameters, such as ventilation and heat losses also have a major impact on the building´s thermal inertia, especially at the lowest occurring outdoor temperatures. The simulations confirm the theory regarding the building heat capacity to prevent a negative impact on indoor thermal comfort. In addition, the indoor air temperature can initially decrease faster than the building framework, especially under the influence of ventilation. This must be taken into account when applying functions for load priority. Calculations indicate that the economical benefits by adapting functions for load priority are primarily for the heat-producer, due to reduced oil dependence and also other system aspects. The current ownership structure in Uppsala provides for a possible obstacle when it comes to expanded use of load priority functions. More incentives for the consumer are needed to provide for an increased usage of load priority functions in their district-heating substations.
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Landelius, Erik, and Magnus Åström. "DISTRICT HEAT PRICE MODEL ANALYSIS : A risk assesment of Mälarenergi's new district heat price model." Thesis, Mälardalens högskola, Akademin för ekonomi, samhälle och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44097.

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Energy efficiency measures in buildings and alternative heating methods have led to a decreased demand for district heating (DH). Furthermore, due to a recent increase in extreme weather events, it is harder for DH providers to maintain a steady production leading to increased costs. These issues have led DH companies to change their price models. This thesis investigated such a price model change, made by Mälarenergi (ME) on the 1st of August 2018. The aim was to compare the old price model (PM1) with the new price model (PM2) by investigating the choice of base and peak loads a customer can make for the upcoming year, and/or if they should let ME choose for them. A prediction method, based on predicting the hourly DH demand, was chosen after a literature study and several method comparisons were made from using weather parameters as independent variables. Consumption data from Mälarenergi for nine customers of different sizes were gathered, and eight weather parameters from 2014 to 2018 were implemented to build up the prediction model. The method comparison results from Unscrambler showed that multilinear regression was the most accurate statistical modelling method, which was later used for all predictions. These predictions from Unscrambler were then used in MATLAB to estimate the total annual cost for each customer and outcome. For PM1, the results showed that the flexible cost for the nine customers stands for 76 to 85 % of the total cost, with the remaining cost as fixed fees. For PM2, the flexible cost for the nine customers stands for 46 to 61 % of the total cost, with the remaining as fixed cost. Regarding the total cost, PM2 is on average 7.5 % cheaper than PM1 for smaller customer, 8.6 % cheaper for medium customers and 15.9 % cheaper for larger customers. By finding the lowest cost case for each customer their optimal base and peaks loads were found and with the use of a statistical inference method (Bootstrapping) a 95 % confidence interval for the base load and the total yearly cost with could be established. The conclusion regarding choices is that the customer should always choose their own base load within the recommended confidence interval, with ME’s choice seen as a recommendation. Moreover, ME should always make the peak load choice because they are willing to pay for an excess fee that the customer themselves must pay otherwise.
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Buddee, Ingrid. "Utveckling av lastmodell för Uppsala fjärrvärmenät." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-229937.

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The aim of this study was to develop a load prognosis model for Uppsala district heating system to be used as a tool for heat production optimization. The methodwas to build three models for the different customer types; housing, industry andoffices and then scale them for the total system using data from Uppsala districtheating system. The heat load consists of two parts, one that is temperaturedependent and one that is dependent of the social behavior of the customers. Thetemperature part was modelled with an ARX model using an outdoor temperatureprognosis as input signal. The social behavior part was modelled using the mean ofthe social behavior from some days before and additionally by distinguishing betweenweekdays and weekends. The outcome was a model that would produce a prognosisfor the heat load for each customer type. The total model for the whole districtheating system was less accurate, but still usable. All models developed are howeverrelying on the quality of the available weather prognosis. The benefit of a precise loadprognosis is to facilitate production planning and optimization. Accurate predictions ofthe heat demand, especially in the case of peak load, will result in better productionplanning and thus cost efficiency.
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Svensson, Kenny. "Evaluation of a Machine Learning Approach To Heat Prediction." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2753.

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This is a report about machine learning in the field of computer science. The problem handled is prediction of energy consumption in district heating systems. Prediction of energy consumption in district heating systems is a delicate problem because of the social behaviours, weather and distribution time that has to be accounted for. One algorithm is introduced and three different experiments are made to determine if the algorithm is useful. The results from the experiments were good. This report differs in approach to the problem then other reports found in this field. The difference is that this report tries to handle social behaviours and looks at a decentralized view of the problem instead of centralized.<br>Denna rapport är om maskininlärning och hur mna kan använda en maskinlärningsalgoritm för att förutspå konsumption i fjärrvärmenät. Rapporten skiljer sig markant i synsätt jämt emot andra rapporter i ämnet genom att den tittar även på de sociala faktorerna.
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Eriksson, Stina. "Optimering av framledningstemperaturen i ett fjärrvärmenät genom lastmodellering och simulering." Thesis, Högskolan i Gävle, Energisystem och byggnadsteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-32790.

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I Sverige är fjärrvärme den vanligaste uppvärmningsformen. Vatten värms upp i en fjärrvärmeanläggning och distribueras genom nedgrävda rör i marken, också kallat fjärrvärmenätet. En tillförlitlig energimodell anses vara ett bra och viktigt hjälpmedel för analyser av värmeförluster som uppstår i ett fjärrvärmenät vid distribueringen av det heta vattnet. Sandviken Energis styrning av framledningstemperaturen sker idag utifrån en inställd styrkurva som tar hänsyn till vad det är för utetemperatur. Det var av intresse för studien att jämföra denna styrning med en simulerad framledningstemperatur och identifiera övertemperaturer i Sandviken Energis fjärrvärmenät i Sandviken. Detta gjordes utifrån att undersöka hur olika faktorer påverkade värmebehovet. De påverkande faktorer som studerats i detta examensarbete var följande: utetemperatur, månad, tid på dygnet och vindhastighet. Mätdata gällande valda påverkande faktorer hämtades för perioderna 2015 till och med 2019, analyserades och indelades för att se deras påverkan på värmelasten. Utifrån indelningen av faktorerna utvanns ekvationer från deras effektkurvors trendlinjer. Ekvationerna användes för att skapa en simuleringsmatris för styrningen. En egenskapad masterekvation simulerade den ideala styrningen utifrån simuleringsmatrisen och de krav på påverkande faktorer som ställs av ett exempel-år. Den ideala styrningen beräknades om till en ideal framledningstemperatur och jämfördes därefter med den verkliga framledningstemperaturen. Tillsammans med en värmeförlustsimulering i NetSim, som resulterade i vad sparad energi per grad är värd, kunde besparingspotentialen beräknas. Resultatet visar på att en besparingspotential på 261 MWh är möjlig att uppnå vid en sänkning av framledningstemperaturen för att utesluta övertemperaturer i fjärrvärmenätet, vilket är en minskning med ca 1,8 % jämfört med det verkliga året. Detta skulle motsvara en besparing på ca 70 000 SEK genom en förändring av styrningen. En minskad framledningstemperatur kommer påverka resten av systemet positivt, bland annat för att returtemperaturen kommer minska, rökgaskondenseringens och pannornas effektivitet öka samt minskade utsläpp i form av bland annat CO2, för att nämna några exempel.<br>In Sweden district heating is the most common form of heating. Water is heated in a district heating plant and distributed through buried pipelines in the ground, also called the district heating network. A reliable energy model is considered to be a good and important tool for analysis of heat losses that occur in a district heating network when the hot water is distributed. Sandviken Energi’s control of the supply temperature is based today on a set control curve that takes into account what the outdoor temperature is. It was of interest to this study to compare this control with a simulated supply temperature and identify overtemperatures in Sandviken Energi’s district heating network in Sandviken. This was done on the basis of examining how different factors affected the heat demand. The influencing factors studied in the thesis were the following: outdoor temperature, month, time of day and wind speed. Measurement data on selected influencing factors were collected for the periods 2015 through 2019, analyzed and subdivided to see their effect on the heat load. From the subdivision of the factors, equations were extracted from the trend lines of their effect curves. The equations were used to create a simulations matrix for the control. A custom master equation simulated the ideal control based on the simulation matrix and the demands on influencing factors set by an example year. The ideal control was recalculated to an ideal supply temperature and then compared with the actual supply temperature. Together with a heat loss simulation in NetSim, which resulted in what energy saved per degree is worth, the savings potential could be calculated. The result shows that a saving potential of 261 MWh is possible to achieve by lowering the supply temperature to exclude excess temperatures in the district heating network, which is a decrease of about 1.8 % compared to the real year. This would correspond to a savings of about 70 000 SEK through a change in control. A reduced supply temperature will have a positive impact on the rest of the system, including reducing the return temperature, increasing the efficiency of flue gas condensation and boilers, and reducing emissions such as CO2, to name a few examples.
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Book chapters on the topic "District heating load prediction"

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Jia, Meng, Chunhua Sun, Shanshan Cao, and Chengying Qi. "District Heating System Load Prediction Using Machine Learning Method." In Environmental Science and Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9524-6_61.

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Hu, Xiaoxue, Yanfeng Liu, Yong Zhou, and Dengjia Wang. "Prediction and Factors Determination of District Heating Load Based on Random Forest Algorithm." In Environmental Science and Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9528-4_90.

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Bianchi, Federico, Francesco Masillo, Alberto Castellini, and Alessandro Farinelli. "XM_HeatForecast: Heating Load Forecasting in Smart District Heating Networks." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_53.

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Domański, Paweł D., and Marcin Więcławski. "Memory-Based Prediction of District Heating Temperature Using GPGPU." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15796-2_4.

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Koščák, Juraj, Rudolf Jakša, Rudolf Sepeši, and Peter Sinčák. "Daily Temperature Profile Prediction for the District Heating Application." In Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00542-3_37.

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Wang, Ruiting, Fulin Wang, Zhaohan Nan, Minjie Xiao, and Aijun Ding. "Precise Control for Heating Supply to Households Based on Heating Load Prediction." In Environmental Science and Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9524-6_89.

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Cao, Shanshan, Hua Zhao, Xin Xie, and Xiaolin Liu. "District Heating System Adjustment Theoretical Based on Heat Users’ Real Load." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39581-9_57.

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Chicherin, Stanislav, Andrey Zhuikov, Mikhail Kolosov, Lyazzat Junussova, Madina Aliyarova, and Aliya Yelemanova. "Controlling Temperatures in Low-Temperature District Heating: Adjustment to Meet Fluctuating Heat Load." In XIV International Scientific Conference “INTERAGROMASH 2021”. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80946-1_29.

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Bianchi, Federico, Alberto Castellini, Pietro Tarocco, and Alessandro Farinelli. "Load Forecasting in District Heating Networks: Model Comparison on a Real-World Case Study." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-37599-7_46.

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Qi, Peiwen, and Wenzhong Gao. "Research on Summer Cooling Load Prediction of Combined Cooling, Heating and Power System." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7047-2_5.

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Conference papers on the topic "District heating load prediction"

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Hajri, Alaeddine, Roberto Garay-Marinez, Ana M. Macarulla, and Mohamed Amin Ben Sassi. "Data-Driven Model for Heat Load Prediction in Buildings Connected to District Heating Networks." In 2024 9th International Conference on Smart and Sustainable Technologies (SpliTech). IEEE, 2024. http://dx.doi.org/10.23919/splitech61897.2024.10612340.

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Strušnik, Dušan, and Jurij Avsec. "Artificial Intelligence for Predicting District Heating Load from Different Sources Based on Environmental Temperature." In 2024 4th International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME). IEEE, 2024. https://doi.org/10.1109/iceccme62383.2024.10796176.

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Himasree, K., Altaf Q. H. Badar, Khai Phuc Nguyen, and Pradita Octoviandiningrum Hadi. "Prediction of Heating and Cooling Load Using Machine Learning Techniques." In 2024 23rd National Power Systems Conference (NPSC). IEEE, 2024. https://doi.org/10.1109/npsc61626.2024.10987036.

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Navarro, M. "Life Time Prediction of Cross Linked Polypropylene Coatings." In CORROSION 2016. NACE International, 2016. https://doi.org/10.5006/c2016-07742.

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Abstract Heat Shrink Sleeve (HSS) coatings technology is based on radiation cross-linking of Polyethylene and Polypropylene sheets in combination with multiple types of adhesive chemistries. Heat Shrink sleeves have been and continue being used for the corrosion protection of Oil, Gas, Water and District Heating pipelines. This paper addresses the laboratory studies of the long term thermal, oxidative and hydrolytic stability of radiation cross-linked polyolefin as used in Heat Shrink Sleeves. Studies are presented on the results of long term heat aging and predicted life expectancy based upon Arrhenius plots. Analysis such as oxidation induction time, dynamical mechanical properties, peel adhesion and physical mechanical testing are discussed as test methods to validate and predict long term stability and projected life expectancy for in-service on operating pipelines.
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Zhou, Yaxin, Feng Li, and Yonghui Chen. "Short-Term Load Prediction Using CNN-LSTM Network and Sparrow Search Algorithm for Combined Cooling Heating and Power Systems." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606848.

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Tasić, Milica, Ivan Ćirić, Marko Ignjatović, and Dušan Stojiljković. "Comparison of Various Machine Learning Methods for Heat Load Prediction in District Heating System." In XVII International Conference on Systems, Automatic Control and Measurements. University of Niš, Faculty of Electronic Engineering, Faculty of Mechanical Engineering, Niš, 2024. https://doi.org/10.46793/saum24.169t.

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This paper explores the potential application of supervised machine learning for predicting the energy performance of the district heating system at the Faculty of Mechanical Engineering in Niš. The operation of this heating system is controlled automatically, while the energy performance is monitored through a SCADA system. Although the SCADA system provides detailed data insights, optimization decisions aimed at saving energy and reducing costs are made by the heating plant operator. The objective of this research is to apply two different machine learning methods, artificial neural networks and random forest analysis, to predict the heating load based on a set of various energy indicators used as input parameters, derived from the SCADA system of the heating plant. The predictions were made for a period spanning 15 days, and the results were obtained using different algorithms of neural networks and random forest analysis in the MATLAB software tool. The primary goal was to present the results of applying these two machine learning methods for heating load prediction, and to compare them by providing a detailed analysis of their performance.
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Behrens, Fabian, Stefan Leiprecht, Jonas Brantl, and Matthias Finkenrath. "Temporal Fusion Transformer for thermal load prediction in district heating and cooling networks." In 63rd International Conference of Scandinavian Simulation Society, SIMS 2022, Trondheim, Norway, September 20-21, 2022. Linköping University Electronic Press, 2022. http://dx.doi.org/10.3384/ecp192047.

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Accurate forecasting of thermal loads is a critical factor for operating district heating and cooling networks economically, efficiently and with minimized emissions. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and fossil generation, in particular in peaking units, can be avoided. Machine learning has already proven to be an efficient tool for time series forecasting in this context. One recent advancement in machine learning is the "Temporal Fusion Transformer" (TFT), which shows especially good results in the area of time series forecasting. This paper examines the performance of TFT in the concrete context of thermal load forecasting for district heating and cooling networks. First, a brief summary of differences between TFT and other machine learning methods is given. Secondly, it is described how the method can be adopted to train a machine learning model for thermal load forecasting. The data to train and evaluate the neural network is based on 8 years of hourly operating data made available from the district heating network of the city of Ulm in Germany. The presented technique is used to produce 72 hours of heating load forecasts for three different district heating grids in the city of Ulm. The results are compared to forecasts of other machine learning methods that have been previously made as part of the publicly funded research project "deepDHC", in order to evaluate if TFT is an improvement to further reduce forecasting uncertainties.
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Yang, Hongying, Shuanglong Jin, Shuanglei Feng, Bo Wang, Fei Zhang, and Jianfeng Che. "Heat Load Forecasting of District Heating System Based on Numerical Weather Prediction Model." In 2015 2nd International Forum on Electrical Engineering and Automation (IFEEA 2015). Atlantis Press, 2016. http://dx.doi.org/10.2991/ifeea-15.2016.1.

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Kato, Kosuke, Masatoshi Sakawa, Keiichi Ishimaru, Satoshi Ushiro, and Toshihiro Shibano. "Heat load prediction through recurrent neural network in district heating and cooling systems." In 2008 IEEE International Conference on Systems, Man and Cybernetics (SMC). IEEE, 2008. http://dx.doi.org/10.1109/icsmc.2008.4811482.

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Renuke, Avinash, Stavros Vouros, and Konstantinos Kyprianidis. "Machine learning assisted adaptive heat load consumption forecasting in district heating network." In 64th International Conference of Scandinavian Simulation Society, SIMS 2023 Västerås, Sweden, September 25-28, 2023. Linköping University Electronic Press, 2023. http://dx.doi.org/10.3384/ecp200050.

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District heating system often consists of a long, complex network of piping carrying heat from a power plant to the consumers. The supply temperature from the plant is either controlled by the operator from experience or a predefined curve based on the outdoor temperature. An optimized supply temperature which would be lower than the one obtained traditionally would lead to lower heat loss and reduced peak load on the power plant. In this paper, we investigate the machine learning models for heat load forecasting which is a crucial parameter in the optimizing process. Models are generated using supervised machine learning algorithms: Linear models (Linear Regression, Ridge and Gaussian Process Regressor), Random Forest Regressor, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) recurrent neural network (RNN). Data-driven models are used extensively in the literature to predict heat load prediction based on the weather and the time effect on a fixed training set, however, in this study, we model the heat load in the network in real-time scenarios i.e., adaptive training and forecasting. The model is adaptively updated as well as the training of the machine learning model in real time. It provides a “plug-and-play” solution for real-time prediction without significant pre-tuning requirements. The results of all the models are compared with various time horizons i.e., 6 hrs, 10 hrs, 24 hrs and 1 week, using the district heating data obtained for the city of Vasteras in Sweden. The performance of the prediction algorithms is evaluated using Mean Absolute Percentage Error (MAPE) and Root Mean Squared Error (RMSE). An algorithm with the best accuracy is selected based on the performance comparison. Also, models suitable for short-term and long-term forecasting are discussed towards the end of the article
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