Artykuły w czasopismach: „Residential demand modelling”

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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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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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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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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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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 establishing the measured demand series. NSRPM has been applied to generating local residential demand. However, this model has not been validated for a real distribution network with residential demand aggregation, or compared to traditional methods (which is dealt with here). This paper compares the results of synthetic stochastic demand series (calculated using NSRPM applied to determining pressure and flow rate) to results obtained using traditional simulation methods using the curve of hourly variation in demand and to actual pressure and flow rate measurements. The Humaya sector of Culiacan, Sinaloa, Mexico, was used as study area.

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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) system. The research described in this article investigated the use of a new approach to improve the computational quality and accuracy of the heating energy demands of UFAD systems using an integrated zonal/multi-zone model. Several case studies were carried out, and the results demonstrate not only the advantages of UFAD systems used in a residential house in terms of energy saving, but also the importance of thermostat location in the prediction of building energy consumption. Additionally, the results indicate that the conventional single/multi-zone models are not appropriate to use for UFAD systems in the building energy demand predictions.

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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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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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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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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 models with an application focus on water distribution systems owing to the random and temporal behavior of water demand in this system type. In this study, a water-demand stochastic simulation model in residential buildings is proposed, which encompasses the behavioral modelling of users and their interaction with the system to improve the design process of water distribution systems. Therefore, geographical and population factors (quantity, distribution, and organization) were considered for the behavioral modelling of users; regarding the system modelling, aspects related to the hydraulic system were considered, such as the relation between system components, the type of sanitary appliance, and the number of available devices. Different simulations—with several different types of showers—were conducted using the proposed model. Comparing the flows obtained from the simulation and from the Brazilian standard, for all system components, the decrease in the project flow rate varied from 4% to 61%. In terms of material consumption regarding the pipe (PVC), the decrease varied from 25% to 63%. Practical application: When assessing potential designs for components in water distribution systems in buildings robust information is required for water demand across different time scales. The use of simulation models represents an important advance for the dimensioning process of these components, since it is possible to know a wider range of information about the system demand possibilities. The use of this type of model, as discussed in this article, will equip the designer with an enhanced decision making capacity.

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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 and learning algorithms have been tested. The paper presents those from the tested models, which were characterized by the highest precision of operation.

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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 building’s potential to operate flexibly, presenting a metric based on measured temperature drop in a dwelling under standard conditions after heating is switched off, using smart meter and internal temperature data. A result was derived for 96% of 193 homes within a test dataset, mean temperature drop of 1.5 °C in 3 h at 15 °C inside-outside temperature differential. An empirical flexibility metric may support decision making and decarbonisation. For households it may support the transition to heat pumps, enabling time of use costs and tariffs to be better understood and system to be specified by installers. Electricity system stakeholders, such as aggregators and DNOs may use it to identify the potential for demand response, managing local networks, infrastructure and aggregation.

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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 Response (DR) programmes applied to residential heating for different renewables penetration and power system configuration scenarios. To that end, a bottom-up model for residential heat demand and flexible electric heating systems (heat pumps and electric water heaters) is developed and directly integrated into Dispa-SET, an existing unit commitment optimal dispatch model of the power system. The integrated model is calibrated for the case of Belgium and different simulations are performed varying the penetration and type of residential heating technologies, installed renewables capacity and capacity mix. Results show that, at country level, operational cost could be reduced up to €35 million and curtailment up to 1 TWh per year with 1 million flexible electric heating systems installed. These benefits are significantly reduced when nuclear power plants (non-flexible) are replaced by gas-fired units (flexible) and grow when more renewable capacity is added. Moreover, when the number of flexible heating systems increases, a saturation effect of the flexibility is observed.

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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 the more detailed distribution system a network water quality model is needed that is based on short time scale demands that considers the effect of dispersion and transients. Demand models that provide stochastic residential demands per individual home and on a one-second time scale are available. A stochastic demands based network water quality model needs to be developed and validated with field measurements. Such a model will be probabilistic in nature and will offer a new perspective for assessing water quality in the DWDS.

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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. Even if there was enough computing power within the models, energy consumption is collected through surveys and as a result only a sample of a region is captured. These challenges have forced energy modellers to categorise households that have similar characteristics. Different researchers choose different methods for categorising the households. Some researchers choose to categorise households by location and climate, others choose housing types while others choose quintiles. Currently, there is no consensus on which categorisation method takes precedence over others. In these myriad ways of categorising households, the determining factor employed in each method is what is assumed to be the driver of energy demand in that particular area of study. Many researchers acknowledge that households’ income, preferences and access to certain fuels determine how households use energy. Although many researchers recognise that income is the main driver of energy demand in the residential sector, there has been no energy modelling study that has tried to categorise households by income in South Africa. This paper chose to categorise households by income because income is taken to be the main driver of energy demand in the urban residential sector. Gauteng province was chosen as a case study area for this paper. The Long-range Energy Alternatives Planning System (LEAP) is used as a tool for such analysis. This paper will further reveal how the dynamics of differing income across the residential sector affects total energy demand in the long run. The households in Gauteng are classified into three income categories – high, middle and low income households. In addition to different income categories, the paper further investigates the energy demand of Gauteng’s residential sector under three economic scenarios with five energy demand scenarios. The three economic scenarios are first economic scenario (ECO1), second economic scenario (ECO2) and third economic scenario (ECO3). The most distinguishing factor between these economic scenarios is the mobility of households from one income band to the next.The model results show that electricity demand will be high in all the three economic scenarios. The reason for such high electrical energy demand in all the economic scenarios compared to other fuels is due to the fact that among all the provinces, Gauteng households have one of the highest electricity consumption profiles. ECO2 showed the highest energy demand in all the five energy demand scenarios. This is due to the fact that the share of high income households in ECO2 was very high, compared to the other two economic scenarios. The favourable energy demand scenarios will be the Energy Efficiency and MEPS scenarios due to their ability to reduce more energy demand than other scenarios in all the three economic scenarios.

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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 installing new monitoring devices and customer interruption, through a stochastic process which is a combination of patterns of active occupancy, the outdoor ambient light characteristic and daily activity profiles. Due to the model is based on time use survey data in UK, the outdoor irradiance and appliance configuration are adapted to fit for the Thailand case study. In order to verify the model, the synthetic load profiles by CREST Demand Model is compared against measured data from the actual monitoring in a real low voltage network in Thailand. The results show that it is promising to apply the high resolution demand model by CREST to simulate the domestic electricity demand profiles in Thailand.

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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 ministries and directorates and Sudan Central Bank. “e!Sankey 4 pro” software is used to develop the Sankey diagram. The main primary types of energy in Sudan are oil, hydro, biomass, and renewable energy. Sudan has a surplus of gasoline, petroleum coke, and biomass and deficit in electric power, gasoil, jet oil, and LPG. The surplus of gasoline is exported; however, the petroleum coke is kept as reserve. The deficit is covered by import. The overall useful energy is 76% and the loss is 24%. The useful energy is distributed among residential (38%), transportation (33%), industry (12%), services (16%), and agriculture (1%) sectors.

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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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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 time frame, that of a few years ahead. Investigations here are aimed at policy support, although in a more mid-term horizon, we address issues such as investment planning and pricing. In this paper, we develop and evaluate statistical and neural network approaches for this mid-term forecasting of final energy and electricity for the residential sector in six EU countries (Germany, the Netherlands, Sweden, Spain, Portugal and Greece). Various possible approaches to model the explanatory variables used are presented, discussed, and assessed as to their suitability. Our end goal extends beyond model accuracy; we also include interpretability and counterfactual concepts and analysis, aiming at the development of a modelling approach that can provide decision support for strategies aimed at influencing energy demand.

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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 lines that comprise the transport portion of a typical municipal pipe system, a skeletonised network model with a top-down approach of demand pattern allocation, a hydraulic time step of 1 h, and a pure advection-reaction water quality model will usually suffice. For the smaller diameter lines that comprise the distribution portion of a municipal pipe system, an all-pipes network model with a bottom-up approach of demand pattern allocation, a hydraulic time step of 1 min or less, and a water quality model that considers dispersion and transients may be needed. Demand models that provide stochastic residential demands per individual home and on a one-second time scale are available. A stochastic demands based network water quality model needs to be developed and validated with field measurements. Such a model will be probabilistic in nature and will offer a new perspective for assessing water quality in the drinking water distribution system.

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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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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 methods, is a difficult parameter to estimate. Commonly, high-resolution field data is used for stochastic modelling of residential water demand which may not be always available. The paper reviews important residential water demand models in view of their applicability in building water supply system design. The irregular nature of water demand in buildings is due to uncertainty in water-use behaviour of users at fixture level. Use of soft-computing techniques can provide an advantage over the other methods in modelling such behaviour. The paper also discusses reliability of building water supply systems and applicability of some common indices for estimating reliability of building water supply systems.

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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 investigate large residential water demand records of different characteristics. The analysis indicates that the studied characteristics of the marginal distribution of water demand vary among households as well as on the basis of different time intervals. Both month-to-month and hour-to-hour analysis reveal that the mean value and the probability of no demand exhibit high variability while the changes in the shape characteristics of the marginal distributions of the nonzero values are significantly less. The investigation of performance of 10 probabilistic models reveals that Gamma and Weibull distributions can be used to adequately describe the nonzero water demand records of different characteristics at both time scales.

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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 can satisfy residential demands with conditions based on the city of Adelaide, Australia. Physical conditions include a relatively flat topography and a temperate climate, with annual rainfall being around 500 mm. The level of water-self-sufficiency that may be achieved within a reuse development in this environment is estimated at around 60%. A case study is also presented in which a conventional development is re-designed on the basis of the reuse model. Costing of the two developments indicates the reuse scenario is only marginally more expensive. Such costings however do not include the benefit to upstream and downstream environments resulting from reduced demand and discharges. As governments look to developers to recover system augmentation and environmental costs the economics of such approaches will increase.

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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 approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.

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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 and commercial areas to calculate the total PV production for each type of load to achieve a balanced energy area, considering (i) the number of buildings, (ii) the type of load, (iii) the peak load, and (iv) the total PV array area in m2 per building. The results of the modelling study using real data demonstrate that the anticipated total PV production in residential and commercial areas is sufficient to meet local peak demand, and there is an excess of power that can either be stored locally or exported to the grid.

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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 base reference is modeled. Two spatial data sets with information on the construction period of residential buildings, aggregated on municipality sections and hectare grid cells, are used to show how census-based spatial data sets can enhance the approach. Partial results from all three models are validated against reported regional data on heat demand as well as against gas consumption of a municipality. All three models overestimate reported heat demand on regional levels by 16% to 19%, but underestimate demand by up to 8% on city levels. Using the hectare grid cells data set leads to best prediction accuracy values at municipality section level, showing the benefit of integrating this high detailed spatial data set on building age.

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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 strategies allow end-users to control their consumption based on the bidirectional communication with the system operator, improving not only the efficiency of the system but more importantly, the residential sector-associated costs from the end-users’ side. The demand load data were analysed and categorised in order to form profiles and better understand the consumption patterns. Different methods were tested in order to come up with the optimal result. The Auto Regressive Integrated Moving Average modelling methodology was selected in order to ensure forecasts production on load demand with the maximum accuracy.

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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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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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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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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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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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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 simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

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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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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 more than residential and office buildings. Specifically, cooling demands far outweigh heating demands for these building types. Therefore, buildings in the commercial and transportation sectors would receive greater consideration for energy efficiency and improvements to the cooling system would be a priority. Additionally, the method developed for predicting building energy demand at an urban scale can also be replicated in practice.

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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 three variables: Lighting efficiency, Plug-Load Efficiency, and HVAC systems. The analysis concluded that while the higher the efficiency of lighting and applications, the lower the electric demand. In addition, the type of climate and thermal characteristics of the materials that conform to the building envelope have significant effects on the energetic performance. The adjustment of different energetic measures and its comparison with other climatic zones enable decision-makers to choose the best combination of variables for developing strategies to lower the electric demand towards energy-efficient buildings.

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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. Several calculation methods were combined: building simulation, energy supply technology simulation, heat demand aggregation, and data post-processing. The results showed that the energy pathways would be very dependent on CO2-factors for energy sources, and it is hard to predict accurate CO2-factors. An increasing housing stock development would slightly increase the CO2 emissions towards 2050, although the new buildings used much less energy and the existing buildings underwent renovation. A constant housing stock would yield a 22–27% reduction of CO2 emissions by 2050. This showed that implementing stricter building codes had a lower impact on the total CO2 emissions than CO2-factors and energy technologies. The focus should lie on energy supply systems.

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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 modelling and simulation of the energy flows of the house to determine the cooling thermal load. The study is useful in providing a better solution for a sustainable future by simulating with different design modifications of the house. This study may be extended to focus on the choice of constructional materials, so that good temperature and lower cooling load are attained. A case study of a residential building is situated in the coastal district of Andhra Pradesh, India and located at Latitude (17.68o N) and longitude (83.21oE) is considered to find cooling load through TRNSYS 16. Keywords: Cooling load, Tropical region, Simulation, ventilation

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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 point potentially optimising the use of all available water sources. It also allows correct sizing of such water systems and their components to reduce scheme water demand. A decision support tool based on water balance modelling was developed that considers such water options at the household scale. Application of this tool to eight scenarios for both large and small house lots shows that using alternate water sources individually can result in significant scheme water savings. However by integrating these sources additional scheme water saving can be made.

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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, (ii) washing machine, (iii) personal computer, and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning were used. The simulations included over 10,000 ANNs with different architecture (number of neurons and structure of their connections), type and number of input variables, formulas of activation functions, training algorithms, and other parameters. The research confirmed the possibility of using ANNs to model the disaggregation of electricity consumption based on low frequency data, and suggested ways to build highly optimised models.

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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 review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.

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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 cooking is projected to be 26.5 million GJ, 16.3 million GJ, 45.2 million GJ and 58.2 million GJ for business as usual (BAU), low growth rate (LGR), medium growth rate (MGR) and high growth rate (HGR) scenarios, respectively. To substitute LPG with electricity in the cooking sector by 2035, an additional 1207 MW, 734 MW, 2055 MW and 2626 MW hydropower installation is required for BAU, LGR, MGR and HGR scenarios, respectively. In the MGR scenario, substituting LPG with electricity could save from $21.8 million (2016) to $70.8 million (2035) each year, which could be used to develop large-scale hydropower projects in the long term.

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