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Journal articles on the topic 'Residential demand modelling'

Author: Grafiati

Published: 6 September 2023

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1

Dilaver, Zafer, and Lester C. Hunt. "Modelling and forecasting Turkish residential electricity demand." Energy Policy 39, no. 6 (2011): 3117–27. http://dx.doi.org/10.1016/j.enpol.2011.02.059.

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2

Assimakopoulos, V. "Residential energy demand modelling in developing regions." Energy Economics 14, no. 1 (1992): 57–63. http://dx.doi.org/10.1016/0140-9883(92)90025-9.

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3

Worthington, Andrew C., and Mark Hoffman. "AN EMPIRICAL SURVEY OF RESIDENTIAL WATER DEMAND MODELLING." Journal of Economic Surveys 22, no. 5 (2008): 842–71. http://dx.doi.org/10.1111/j.1467-6419.2008.00551.x.

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4

Atalla, Tarek N., and Lester C. Hunt. "Modelling residential electricity demand in the GCC countries." Energy Economics 59 (September 2016): 149–58. http://dx.doi.org/10.1016/j.eneco.2016.07.027.

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5

Alcocer Yamanaka, Víctor Hugo, and Velitchko G. Tzatchkov. "Neyman-Scott-based water distribution network modelling." Ingeniería e Investigación 32, no. 3 (2012): 32–36. http://dx.doi.org/10.15446/ing.investig.v32n3.35937.

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Residential water demand is one of the most difficult parameters to determine when modelling drinking water distribution networks. It has been proven to be a stochastic process which can be characterised as a series of rectangular pulses having set intensity, duration and frequency. Such parameters can be determined using stochastic models such as the Neyman-Scott rectangular pulse model (NSRPM). NSRPM is based on resolving a non-linear optimisation problem involving theoretical moments of the synthetic demand series (equiprobable) and of the observed moments (field measurements) statistically

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6

Megri, Ahmed Cherif, and Yao Yu. "Study of residential underfloor air distribution (UFAD) systems using a new modelling approach." Indoor and Built Environment 26, no. 1 (2016): 5–20. http://dx.doi.org/10.1177/1420326x15597544.

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Although single/multi-zone thermal models have their own advantages, like simple and fast computations of building energy demand, the accuracy of these models is problematic. The assumption of a uniform room temperature reduces the accuracy of the final energy demand results. In fact, the single/multi-zone thermal models are not able to predict indoor thermal behaviours or building energy demands accurately, if a non-uniform environment in a room or building is created by a single or multiple heating, ventilation, and air conditioning (HVAC) systems, i.e. an underfloor air distribution (UFAD)

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Starr, Claudia, Thomas G. Cowing, and David L. McFadden. "Microeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand." Journal of the Operational Research Society 37, no. 8 (1986): 823. http://dx.doi.org/10.2307/2581969.

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8

Chatterjee, Samprit, Thomas G. Cowing, Daniel L. McFadden, and Paul C. Stern. "Macroeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand." Journal of Business & Economic Statistics 3, no. 4 (1985): 413. http://dx.doi.org/10.2307/1391737.

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Starr, Claudia. "Microeconomic Modelling and Policy Analysis: Studies in Residential Energy Demand." Journal of the Operational Research Society 37, no. 8 (1986): 823–24. http://dx.doi.org/10.1057/jors.1986.145.

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10

Ben Zaied, Younes, and Marie Estelle Binet. "Modelling seasonality in residential water demand: the case of Tunisia." Applied Economics 47, no. 19 (2015): 1983–96. http://dx.doi.org/10.1080/00036846.2014.1002896.

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11

Ferreira, Tiago de VG, and Orestes M. Goncalves. "Stochastic simulation model of water demand in residential buildings." Building Services Engineering Research and Technology 41, no. 5 (2019): 544–60. http://dx.doi.org/10.1177/0143624419896248.

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Over the years, researchers have been conducting studies to investigate the water consumption profile in buildings; these studies have contributed to the accumulation of knowledge regarding the correct sizing of hydraulic systems in buildings. In the context of the methods for the characterization of system demand or loading values, the procedures commonly employed to obtain the project flow rate were primarily proposed in the mid-20th century. These models require revision and adaptation to the current water consumption values. In recent years, certain researchers have proposed simulation mod

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12

Jasiński, Tomasz. "Modelling of electricity demand in residential buildings using artificial neural networks." E3S Web of Conferences 49 (2018): 00048. http://dx.doi.org/10.1051/e3sconf/20184900048.

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Electricity is the basis for the functioning of modern society. It is used for many purposes, including HVAC systems. Information on future electricity demand is an important element from the point of view of both the real estate user and other entities on the energy market. The study forecasts the demand for electricity on the basis of data from over 12,000 buildings. The model was created using one of the tools from the area of artificial intelligence - neural networks. Over 15,000 networks differing in architecture, number of nerve cells, activation functions, sets of explanatory variables

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13

Crawley, Jenny, Despina Manouseli, Peter Mallaburn, and Cliff Elwell. "An Empirical Energy Demand Flexibility Metric for Residential Properties." Energies 15, no. 14 (2022): 5304. http://dx.doi.org/10.3390/en15145304.

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Shifting from heating using fossil fuel combustion to electrified heating, dominated by heat pumps, is central to many countries’ decarbonisation strategy. The consequent increase in electricity demand, combined with that from electric vehicles, and the shift from non-renewable to renewable generation requires increased demand flexibility to support system operation. Demand side response through interrupting heating during peak demands has been widely proposed and simulation modelling has been used to determine the technical potential. This paper proposes an empirical approach to quantifying a

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14

Magni, Chiara, Alessia Arteconi, Konstantinos Kavvadias, and Sylvain Quoilin. "Modelling the Integration of Residential Heat Demand and Demand Response in Power Systems with High Shares of Renewables." Energies 13, no. 24 (2020): 6628. http://dx.doi.org/10.3390/en13246628.

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The EU aims to become the world’s first climate-neutral continent by 2050. In order to meet this target, the integration of high shares of Renewable Energy Sources (RESs) in the energy system is of primary importance. Nevertheless, the large deployment of variable renewable sources such as wind and photovoltaic power will pose important challenges in terms of power management. For this reason, increasing the system flexibility will be crucial to ensure the security of supply in future power systems. This work investigates the flexibility potential obtainable from the diffusion of Demand Respon

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15

Blokker, E. J. M., J. H. G. Vreeburg, S. G. Buchberger, and J. C. van Dijk. "Importance of demand modelling in network water quality models: a review." Drinking Water Engineering and Science Discussions 1, no. 1 (2008): 1–20. http://dx.doi.org/10.5194/dwesd-1-1-2008.

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Abstract. Today, there is a growing interest in network water quality modelling. The water quality issues of interest relate to both dissolved and particulate substances, with the main interest in residual chlorine and (microbiological) contaminant propagation, respectively in sediment leading to discolouration. There is a strong influence of flows and velocities on transport, mixing, production and decay of these substances in the network. This imposes a different approach to demand modelling which is reviewed in this article. For transport systems the current hydraulic models suffice; for th

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Wong, L. T., and K. W. Mui. "Stochastic modelling of water demand by domestic washrooms in residential tower blocks." Water and Environment Journal 22, no. 2 (2008): 125–30. http://dx.doi.org/10.1111/j.1747-6593.2007.00087.x.

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Senatla, Mamahloko. "Energy demand projections and relevance of income dynamics in Gauteng’s residential sector." Journal of Energy in Southern Africa 22, no. 4 (2011): 31–47. http://dx.doi.org/10.17159/2413-3051/2011/v22i4a3227.

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Energy modelling serves as a crucial tool for informing both energy policy and strategy development. But the modelling process is faced with both sectoral energy data and structural challenges. Among all the sectors, the residential sector usually presents a huge challenge to the modelling profession due to the dynamic nature of the sector. The challenge is brought by the fact that each an every household in a region may have different energy consumption characteristics and the computing power of the available models cannot incorporate all the details of individual household characteristics. E

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18

Pachanapan, Piyadanai, Panupon Trairat, and Surachet Kanprachar. "Synthetic Domestic Electricity Demand in Thailand using A Modified High Resolution Modelling Tool by CREST." ECTI Transactions on Electrical Engineering, Electronics, and Communications 19, no. 2 (2021): 145–54. http://dx.doi.org/10.37936/ecti-eec.2021192.234341.

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A residential electricity demand profile is one of the key roles for investigating the impacts of high penetration of low carbon technologies, such as photovoltaic systems and electric vehicles, on distribution networks. However, it is difficult to identify the true daily electricity consumption of Thailand household, caused by the lack of routine real time demand monitoring and residential electricity meter is normally on monthly which is a low time resolution. In this paper, the CREST Demand Model is employed to simulate a high resolution domestic electricity demand in Thailand, without inst

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19

Rabah, Ali A., Hassan B. Nimer, Kamal R. Doud, and Quosay A. Ahmed. "Modelling of Sudan’s Energy Supply, Transformation, and Demand." Journal of Energy 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/5082678.

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The study aimed to develop energy flow diagram (Sankey diagram) of Sudan for the base year 2014. The developed Sankey diagram is the first of its kind in Sudan. The available energy balance for the base year 2012 is a simple line draw and did not count the energy supply by private and mixed sectors such as sugar and oil industries and marine and civil aviation. The private and mixed sectors account for about 7% of the national grid electric power. Four energy modules are developed: resources, transformation, demand, and export and import modules. The data are obtained from relevant Sudanese mi

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20

Zasina, Damian, and Jarosław Zawadzki. "On the Temporal Variability of Air Pollutants’ Emissions – Case Study of Residential PM10 Emission in Silesian Metropolis." New Trends in Production Engineering 3, no. 1 (2020): 21–29. http://dx.doi.org/10.2478/ntpe-2020-0003.

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AbstractThe paper summarizes previous studies associated with carrying out of the air pollutant emission inventories. There are presented three approaches for obtaining monthly distribution of PM10 air emission: using expert’s judgement, modelling of the heating demand, and temporal disaggregation using the heating degree days (HDD). However some differences due to not considering hot water demand, it can be effectively used for obtaining temporal, and spatiotemporal distributions of air pollutants’ air emissions necessary for air quality modelling.

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21

Sakkas, Nikos, Sofia Yfanti, Costas Daskalakis, Eduard Barbu, and Marharyta Domnich. "Interpretable Forecasting of Energy Demand in the Residential Sector." Energies 14, no. 20 (2021): 6568. http://dx.doi.org/10.3390/en14206568.

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Energy demand forecasting is practiced in several time frames; different explanatory variables are used in each case to serve different decision support mandates. For example, in the short, daily, term building level, forecasting may serve as a performance baseline. On the other end, we have long-term, policy-oriented forecasting exercises. TIMES (an acronym for The Integrated Markal Efom System) allows us to model supply and anticipated technology shifts over a long-term horizon, often extending as far away in time as 2100. Between these two time frames, we also have a mid-term forecasting ti

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22

Blokker, E. J. M., J. H. G. Vreeburg, S. G. Buchberger, and J. C. van Dijk. "Importance of demand modelling in network water quality models: a review." Drinking Water Engineering and Science 1, no. 1 (2008): 27–38. http://dx.doi.org/10.5194/dwes-1-27-2008.

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Abstract. Today, there is a growing interest in network water quality modelling. The water quality issues of interest relate to both dissolved and particulate substances. For dissolved substances the main interest is in residual chlorine and (microbiological) contaminant propagation; for particulate substances it is in sediment leading to discolouration. There is a strong influence of flows and velocities on transport, mixing, production and decay of these substances in the network. This imposes a different approach to demand modelling which is reviewed in this article. For the large diameter

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23

Reade, Samantha, Temesgen Zewotir, and Delia North. "Modelling household electricity consumption in eThekwini municipality." Journal of Energy in Southern Africa 27, no. 2 (2016): 38. http://dx.doi.org/10.17159/2413-3051/2016/v27i2a1340.

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South African municipalities are faced with the challenges of growing demand for services. This study models the energy consumption estimation practice within the Durban municipal area. It was found that an estimation technique that accounts for the seasonal and monthly effects, as well as residential type, predicts monthly individual household electricity consumption with minimum error. Models that were developed may be used to estimate electricity consumption for household billings within a municipality.

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24

Mangalekar, R. D., and K. S. Gumaste. "Residential water demand modelling and hydraulic reliability in design of building water supply systems: a review." Water Supply 21, no. 4 (2021): 1385–97. http://dx.doi.org/10.2166/ws.2021.021.

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Abstract The building water supply system is a fundamental unit in water supply systems as it is directly associated with end users. However, the studies available on its efficient design are limited. Water demand estimation continues to be an important issue in water supply systems' design because of its multifaceted nature. Hunter's curve, or Fixture Unit method, is widely used for estimating the load on plumbing. Regardless of its popularity, it has a few drawbacks and is arbitrarily modified in some plumbing codes. Fixture-use probability, a basic entity in the Fixture Unit and some other

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25

Kossieris, Panagiotis, and Christos Makropoulos. "Exploring the Statistical and Distributional Properties of Residential Water Demand at Fine Time Scales." Water 10, no. 10 (2018): 1481. http://dx.doi.org/10.3390/w10101481.

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Residential water demand consists one of the most uncertain factors posing extra difficulties in the efficient planning and management of urban water systems. Currently, high resolution data from smart meters provide the means for a better understanding and modelling of this variable at a household level and fine temporal scales. Having this in mind, this paper examines the statistical and distributional properties of residential water demand at a 15-minute and hourly scale, which are the temporal scales of interest for the majority of urban water modeling applications. Towards this, we invest

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26

Barton, A. B., and J. R. Argue. "Integrated urban water management for residential areas: a reuse model." Water Science and Technology 60, no. 3 (2009): 813–23. http://dx.doi.org/10.2166/wst.2009.401.

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Global concern over growing urban water demand in the face of limited water resources has focussed attention on the need for better management of available water resources. This paper takes the “fit for purpose” concept and applies it in the development of a model aimed at changing current practices with respect to residential planning by integrating reuse systems into the design layout. This residential reuse model provides an approach to the design of residential developments seeking to maximise water reuse. Water balance modelling is used to assess the extent to which local water resources

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27

Gholami, Roya, Rohit Nishant, and Ali Emrouznejad. "Modeling Residential Energy Consumption." Journal of Global Information Management 29, no. 2 (2021): 166–93. http://dx.doi.org/10.4018/jgim.2021030109.

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Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approache

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28

Shaher, Abdullah, Saad Alqahtani, Ali Garada, and Liana Cipcigan. "Rooftop Solar Photovoltaic in Saudi Arabia to Supply Electricity Demand in Localised Urban Areas: A Study of the City of Abha." Energies 16, no. 11 (2023): 4310. http://dx.doi.org/10.3390/en16114310.

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This paper explores the potential of rooftop solar PV to meet the electricity demand in the urban areas of Abha city, Saudi Arabia (KSA), minimising imports from the grid. A localised energy system for Abha is proposed that considers two types of loads: (i) residential loads with a monthly aggregated energy consumption of 172,440 MWh and an electric demand of 239.5 MW, and (ii) commercial loads with a monthly aggregated energy consumption of 179,280 MWh and an electric demand of 249 MW. The grid currently supplies this load. This paper proposes a PV development planning tool for residential an

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Schwanebeck, Malte, Marcus Krüger, and Rainer Duttmann. "Improving GIS-Based Heat Demand Modelling and Mapping for Residential Buildings with Census Data Sets at Regional and Sub-Regional Scales." Energies 14, no. 4 (2021): 1029. http://dx.doi.org/10.3390/en14041029.

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Heat demand of buildings and related CO2 emissions caused by energy supply contribute to global climate change. Spatial data-based heat planning enables municipalities to reorganize local heating sectors towards efficient use of regional renewable energy resources. Here, annual heat demand of residential buildings is modeled and mapped for a German federal state to provide regional basic data. Using a 3D building stock model and standard values of building-type-specific heat demand from a regional building typology in a Geographic Information Systems (GIS)-based bottom-up approach, a first bas

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Panagiotidis, Paraskevas, Andrew Effraimis, and George A. Xydis. "An R-based forecasting approach for efficient demand response strategies in autonomous micro-grids." Energy & Environment 30, no. 1 (2018): 63–80. http://dx.doi.org/10.1177/0958305x18787259.

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The main aim of this work is to reduce electricity consumption for consumers with an emphasis on the residential sector in periods of increased demand. Efforts are focused on creating a methodology in order to statistically analyse energy demand data and come up with forecasting methodology/pattern that will allow end-users to organize their consumption. This research presents an evaluation of potential Demand Response programmes in Greek households, in a real-time pricing market model through the use of a forecasting methodology. Long-term Demand Side Management programs or Demand Response st

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31

Michalik, G. "Structural modelling of energy demand in the residential sector: 1. Development of structural models." Energy 22, no. 10 (1997): 937–47. http://dx.doi.org/10.1016/s0360-5442(97)00029-7.

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32

Creaco, Enrico, Raziyeh Farmani, Lydia Vamvakeridou-Lyroudia, Steven G. Buchberger, Zoran Kapelan, and Dragan A. Savić. "Correlation or not Correlation? This is the Question in Modelling Residential Water Demand Pulses." Procedia Engineering 119 (2015): 1455–62. http://dx.doi.org/10.1016/j.proeng.2015.08.1006.

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33

Hedegaard, Rasmus Elbæk, Martin Heine Kristensen, Theis Heidmann Pedersen, Adam Brun, and Steffen Petersen. "Bottom-up modelling methodology for urban-scale analysis of residential space heating demand response." Applied Energy 242 (May 2019): 181–204. http://dx.doi.org/10.1016/j.apenergy.2019.03.063.

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34

García-Gusano, Diego, Tadhg O'Mahony, Diego Iribarren, and Javier Dufour. "Lessons for regional energy modelling: enhancing demand-side transport and residential policies in Madrid." Regional Studies 53, no. 6 (2018): 826–37. http://dx.doi.org/10.1080/00343404.2018.1492711.

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35

Chingcuanco, Franco, and Eric J. Miller. "A microsimulation model of urban energy use: Modelling residential space heating demand in ILUTE." Computers, Environment and Urban Systems 36, no. 2 (2012): 186–94. http://dx.doi.org/10.1016/j.compenvurbsys.2011.11.005.

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36

Rosser, Julian F., Gavin Long, Sameh Zakhary, Doreen S. Boyd, Yong Mao, and Darren Robinson. "Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE." ISPRS International Journal of Geo-Information 8, no. 4 (2019): 163. http://dx.doi.org/10.3390/ijgi8040163.

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Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simula

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Athanasiadis, Ioannis N., Alexandros K. Mentes, Pericles A. Mitkas, and Yiannis A. Mylopoulos. "A Hybrid Agent-Based Model for Estimating Residential Water Demand." SIMULATION 81, no. 3 (2005): 175–87. http://dx.doi.org/10.1177/0037549705053172.

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38

Ji, Qunfeng, Yangbo Bi, Mehdi Makvandi, Qinli Deng, Xilin Zhou, and Chuancheng Li. "Modelling Building Stock Energy Consumption at the Urban Level from an Empirical Study." Buildings 12, no. 3 (2022): 385. http://dx.doi.org/10.3390/buildings12030385.

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Quantifying the energy consumption of buildings is a complex and multi-scale task, with the entire process dependent on input data and urban surroundings. However, most urban energy models do not account for the urban environment. This paper employs a physical-based, bottom-up method to predict urban building operating energy consumption, using imported topography to consider shading effects on buildings. This method has proven to be feasible and aligned well with the benchmark. Research also suggests that commercial and transport buildings have the highest energy use intensity, significantly

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Antonopoulos, Ioannis, Valentin Robu, Benoit Couraud, and David Flynn. "Data-driven modelling of energy demand response behaviour based on a large-scale residential trial." Energy and AI 4 (June 2021): 100071. http://dx.doi.org/10.1016/j.egyai.2021.100071.

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Li, Yuanmeng, Yohei Yamaguchi, and Yoshiyuki Shimoda. "Impact of the pre-simulation process of occupant behaviour modelling for residential energy demand simulations." Journal of Building Performance Simulation 15, no. 3 (2022): 287–306. http://dx.doi.org/10.1080/19401493.2021.2022759.

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Sun, Yanming, and Yihua Yu. "Revisiting the residential electricity demand in the United States: A dynamic partial adjustment modelling approach." Social Science Journal 54, no. 3 (2017): 295–304. http://dx.doi.org/10.1016/j.soscij.2017.02.004.

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Lewis, Jim, Kerrie Mengersen, Laurie Buys, et al. "Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand." PLOS ONE 10, no. 7 (2015): e0134086. http://dx.doi.org/10.1371/journal.pone.0134086.

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González, Jorge, Carlos Soares, Mohammad Najjar, and Assed Haddad. "BIM and BEM Methodologies Integration in Energy-Efficient Buildings Using Experimental Design." Buildings 11, no. 10 (2021): 491. http://dx.doi.org/10.3390/buildings11100491.

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Linking Building Information Modelling and Building Energy Modelling methodologies appear as a tool for the energy performance analysis of a dwelling, being able to build the physical model via Autodesk Revit and simulating the energy modeling with its complement Autodesk Insight. A residential two-story house was evaluated in five different locations within distinct climatic zones to reduce its electricity demand. Experimental Design is used as a methodological tool to define the possible arrangement of results emitted via Autodesk Insight that exhibits the minor electric demand, considering

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44

Nord, Natasa, Yiyu Ding, Ola Skrautvol, and Stian Fossmo Eliassen. "Energy Pathways for Future Norwegian Residential Building Areas." Energies 14, no. 4 (2021): 934. http://dx.doi.org/10.3390/en14040934.

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Owing to stricter building energy requirements, future buildings will be characterized by low base loads and occasional high peaks. However, future building areas will still contain existing and historical buildings with high energy demand. Meanwhile, there is a requirement that future building areas should obtain energy from renewable energy sources, while existing buildings need to be transited to renewables. Therefore, the aim of this study was to develop an approach for modelling energy pathways for future Norwegian residential building areas by analyzing different energy supply systems. S

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45

Laxmi, Kornu, and V. V. S. Kesava Rao. "Estimation of Cooling Load of a Residential House using TRNSYS." Applied Research Journal of Science and Technology 2, no. 1 (2020): 1–24. http://dx.doi.org/10.47721/arjst20200201016.

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Development of a country will lead to increased utilization and demand for energy. In this research study, transient simulation technique is implemented and eventually, the cooling load of the house is determined. The study focused to provide cooling load characteristics for a residential house. The factors namely: size and shape, thermophysical properties, window systems, orientation, internal gains, ventilation and infiltration aspects that influence the cooling load are considered in the study. A dynamic simulation software-TRNSYS (Transient Systems Simulation Program) is used for the model

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Hunt, John, Martin Anda, and Goen Ho. "Water balance modelling of alternate water sources at the household scale." Water Science and Technology 63, no. 9 (2011): 1873–79. http://dx.doi.org/10.2166/wst.2011.399.

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Alternate water sources are being implemented in urban areas to augment scheme water supplied by a water utility to homes. These sources include residential wells, rainwater tanks and greywater systems. Greater water efficiency can be achieved when these systems are designed to match a water source to a given demand based on both water quantity and quality parameters. In this way the use of an alternate water source can be maximised and the use of the high quality scheme water minimised. This paper examines the use of multiple alternate water sources sequentially to supply the same demand poin

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Jasiński, Tomasz. "Modelling the Disaggregated Demand for Electricity in Residential Buildings Using Artificial Neural Networks (Deep Learning Approach)." Energies 13, no. 5 (2020): 1263. http://dx.doi.org/10.3390/en13051263.

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The paper addresses the issue of modelling the demand for electricity in residential buildings with the use of artificial neural networks (ANNs). Real data for six houses in Switzerland fitted with measurement meters was used in the research. Their original frequency of 1 Hz (one-second readings) was re-sampled to a frequency of 1/600 Hz, which corresponds to a period of ten minutes. Out-of-sample forecasts verified the ability of ANNs to disaggregate electricity usage for specific applications (electricity receivers). Four categories of electricity consumption were distinguished: (i) fridge,

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Zhang, Lingxi, Nicholas Good, and Pierluigi Mancarella. "Building-to-grid flexibility: Modelling and assessment metrics for residential demand response from heat pump aggregations." Applied Energy 233-234 (January 2019): 709–23. http://dx.doi.org/10.1016/j.apenergy.2018.10.058.

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Rodrigues, Filipe, Carlos Cardeira, João M. F. Calado, and Rui Melicio. "Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review." Energies 16, no. 10 (2023): 4098. http://dx.doi.org/10.3390/en16104098.

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In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The re

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Bhandari, Ramchandra, and Surendra Pandit. "Electricity as a Cooking Means in Nepal—A Modelling Tool Approach." Sustainability 10, no. 8 (2018): 2841. http://dx.doi.org/10.3390/su10082841.

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Cooking energy has an important role in energy demand of Nepal. Over the last decade, import of Liquefied Petroleum Gas (LPG) has increased by 3.3 times as an alternate cooking fuel to kerosene and firewood. The growing subsidy burden to endorse modern fuel switching from traditional energy sources and high import of LPG are challenges for sustainability and energy security. This paper analyzes the future residential cooking energy demand and its environmental and economic impacts from 2015 to 2035 using a Long-range Energy Alternative Planning System (LEAP) tool. In 2035, the LPG demand for c

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