Domestic Electricity Demand Research Articles

Applying trade mechanisms to quantify dynamic GHG emissions of electricity consumption in an open economy - The case of Switzerland

Quantifying greenhouse gas (GHG) emissions from electricity consumption presents a significant challenge due to the variable nature of the generation mix throughout the year and the complex dynamics of cross-border electricity trade. In this paper, we introduce an innovative methodology designed to accurately capture the hourly GHG emissions linked to electricity imports, and thus electricity consumption. Contrary to conventional approaches, our methodology identifies the relevant generation technologies in exporting countries which are activated for export. This selection is based on (i) the classification of the technologies according to their dispatch cost (merit order), and (ii) considers trade mechanisms from economic theory.We apply this methodology to Switzerland, a country characterized by a low-carbon electricity generation mix, which however heavily relies on imports from neighbouring countries, particularly during the winter months. Over the period from 2017 to 2021, we find that 88 % of Switzerland's domestic electricity demand is met by its own generation—primarily hydro and nuclear—while the remaining 12 % is imported, mainly from fossil-fuel-based sources. Consequently, the average annual GHG emission factor for electricity consumption in Switzerland is 98 g-CO2eq/kWh, with 29 % attributed to domestic generation and 71 % to imports. This figure is 40 % higher than the 70 g-CO2eq/kWh estimated using a conventional approach, which typically underestimates the specific impacts of electricity imports.The disparity between these approaches becomes even more pronounced when considering the seasonal and hourly variations in the electricity mix and the resulting emission factors. These insights are particularly crucial in the context of emerging electricity demands, such as heat pumps and electric vehicles, where accurate GHG accounting can significantly influence policy and investment decisions.

Energy Consumption Management of Residential Appliances Based on Load Signatures Decomposition

Nowadays, energy consumption management techniques in the residential side have gained significant importance due to their considerable influence on the control of power flow in distribution networks and especially the possibility of managing a huge part of domestic electrical demand during peak-load hours. Since the customer’s data are recorded in an aggregated form, therefore, in order to apply control approaches, it is necessary to use load pattern evaluation techniques (load signature). These methods capable to decomposition effective features that help control approaches to implement with more accuracy. In this article, features of residential loads have been extracted by using the signature of the aggregated consumer’s demand. Then these features have been evaluated by methods such as logistic regression, k-nearest neighborhood, and decision tree. By assessing the results, it was determined that among the extracted features, the first two features (consumed power and injected harmonics) covered more than 89% of the variance of the entire set, and with the help of using the principal component analysis method, it was determined that by reducing the number of features to 2, a considerable amount of computation is reduced and only about 4% of the accuracy is reduced. Also, the convolutional neural network approach was used to estimate the type of load, and by identifying controllable loads and applying remote home energy management methods, it was found that by increasing participation up to 80%, more than 41% of peak-load consumption could be shifted to off-peak hours.

Assessment of Economic and Environmental Impacts of using Green Hydrogen Gas for Generating Electricity in the KSA

The energy sector in the Kingdom of Saudi Arabia (KSA) faces serious challenges regarding its current energy mix and energy policies. These challenges are even more complex in the sphere of electricity generation. Where on one side, these challenges are attributed to the fast-growing domestic demand for electricity. While on the other side, KSA depends extensively on traditional fossil fuels for generating electricity and hence facing high rates of carbon dioxide (CO2) emissions. To address these challenges, the Kingdom’s 2030 vision opted for economic diversification and decarbonization by encouraging the transition towards using green hydrogen gas for electricity generation as a clean energy source. This attempt has been associated with measures addressing rationalization of the demand side for electricity. The objective of this paper is to explore the economic and environmental viability of using green hydrogen gas for generating electricity in KSA. Working toward this objective, an economic assessment has been applied to five hypothetical cases or scenarios to identify the most cost-effective (least expensive) to run the turbine generator at net zero CO2 emission. In addition, an assessment of the environmental impact has been applied to the same five hypothetical cases or scenarios to identify the most environmentally friendly i.e., help effectively to reduce or minimize the CO2 emissions. The findings of this assessment reject the economic viability of the transition towards using green hydrogen gas for electricity generation in the KSA, where the calculations of the five cases registered an inverse relation between the NPV and the use of green hydrogen gas in electricity generation. These findings confirm the environmental variability of this transition, where the calculations of the five cases registered a positive relation between decarburization and the use of green hydrogen gas in electricity generation. Based on these findings, the economic ramifications and viability of this transition require a thorough investigation addressing economic and non-economic aspects.

Paving the way: Analysing energy transition pathways and green hydrogen exports in developing countries – The case of Algeria

The measures needed to limit global warming pose a particular challenge to current fossil fuel exporters, who must not only decarbonise their local energy systems, but also compensate for the expected decline in fossil fuel revenues. One possibility is seen in the export of green hydrogen. Using Algeria as a case study, this paper analyses how different levels of ambition in hydrogen exports, energy efficiency and fuel switching affect the cost-optimal expansion of the power sector for a given overall emissions reduction path. Despite falling costs for photovoltaics and wind turbines, the results indicate that in countries with very low natural gas prices, such as Algeria, a fully renewable electricity system by 2050 is unlikely without appropriate policy measures. The expansion of renewable energy should therefore start early, given the high annual growth rates required, which will be reinforced by additional green hydrogen exports. In parallel, energy efficiency is a key factor as it directly mitigates CO2 emissions from fossil fuels and reduces domestic electricity demand, which could instead be used for hydrogen production. Integrating electrolysers into the power system could potentially help to reduce specific costs through load shifting. Overall, it seems advisable to analyse hydrogen exports together with local decarbonisation in order to better understand their interactions and to reduce emissions as efficiently as possible. These results and the methodology could be transferred to other countries that want to become green hydrogen exporters in the future and are therefore a useful addition for researchers and policy makers.

Community stochastic domestic electricity forecasting

The domestic sector is a significant energy consumer – accounting for around 40% of global electricity demand – due to household demand diversity and complexity. An accurate and robust estimation of domestic electrical loads, environmental impacts, and energy-efficiency potential is crucial for optimal planning and management of energy systems and applications. However, uncertainties resulting from simplistic socio-technical attributes, microclimatic variations, and oversimplification of the effects of interdependent variables make domestic energy modelling challenging. In this research, a hybrid bottom-up community energy forecasting framework is developed to estimate sub-hourly domestic electricity demand using a combination of statistical and engineering modelling approaches by considering key factors influencing household consumption, including demographic characteristics, occupancy patterns, and the features, ownership, and utilisation patterns of electric appliances. The framework is tested on a community in Wales, UK and validated on an annual, daily, and sub-hourly basis with monitored electricity usage averages derived from the UK Energy Follow-Up Survey and the sub-national electricity consumption datasets. Results closely reflect annual and daily household demand at individual dwellings and aggregated levels, with an estimation accuracy of up to 90%. Moreover, the framework facilitates more reliable sub-hourly demand profiles compared to conventional simulation practices that overestimate daily electricity demand and sub-hourly peaks by up to 15% and 50%, respectively.

Designing Sustainable Domestic Electricity Supply from Renewable Energy Mixes: Application to Java and Bali, Indonesia

Many countries, including Indonesia, have abundant renewable energy sources (RES), but the share of RES in the current national energy supply is still insignificant. The study aimed to investigate and provide the most feasible combinations of RES that meet domestic electricity demand. For Java and Bali, Indonesia, initially, 35 scenarios, given 4 primary RES (solar, wind, hydropower, geothermal) and municipal solid waste, were assessed based on economic and environmental indicators. This explorative data-driven study found that the existing capacity could only meet 51% of the electricity demand. However, the proposed energy mixes could cover 100% of the electricity demand in 2020 with a required capacity of 8.32–19.10 GW, varying on each scenario. The feasible energy mixes can reduce CO2 emissions by 90–94% compared to a fossil energy mix with gas-fired power plants. The installation, and operation and maintenance costs per life cycle can range from 29–50 and 4–16 billion dollars. The wind-based energy mix, with installed capacities of geothermal (1.16 GW), hydropower (2.87 GW), solar (0.003 GW) and municipal solid waste (0.18 GW) in 2020, showed the highest return on investment (139% ROI) and smallest CO2 emission with highest CO2 reduction (94%). This study provides a scientific method of selecting, projecting, and evaluating viable RES combinations for generating electricity without using fossil fuels.

Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish Islands

Achieving emissions reduction targets requires improved energy efficiency to avoid an oversized and excessively expensive electricity network. This can be analysed using hourly demand modelling that captures behaviour profiles, technology types, weather factors and building typologies. Numerous domestic models exist, but whole systems energy modelling, including commercial and industrial demand, are limited by data availability. Time-use survey data has typically been used to model domestic demand- in this work is expanded to also model commercial and industrial electricity-heating for the Scottish islands at an hourly and individual building level. This method is widely applicable for modelling whole system energy demand wherever time-use survey data are available. Combinatorial optimisation has been applied to generate a synthetic population, match individuals to properties and apply construction types to building polygons. SimStock is used for heating and lighting modelling. Validation of the model with 2016 data shows that it reflects longer term trends, with a monthly mean absolute percentage error (MAPE) of 1.6% and an R2 of 0.99. At the hourly level, the MAPE of 6.2% and R2 of 0.87 show the model captures variability needed to combine it with a supply-side model. Dataset accuracy, variability in the date recorded, missing data and unknown data correlations are discussed as causes for error. The model can be adapted for other regions and used to analyse the costs and benefits of energy efficiency measures with a supply-side generation model.

Machine Learning for Geothermal Resource Exploration in the Tularosa Basin, New Mexico

Geothermal energy is considered an essential renewable resource to generate flexible electricity. Geothermal resource assessments conducted by the U.S. Geological Survey showed that the southwestern basins in the U.S. have a significant geothermal potential for meeting domestic electricity demand. Within these southwestern basins, play fairway analysis (PFA), funded by the U.S. Department of Energy’s (DOE) Geothermal Technologies Office, identified that the Tularosa Basin in New Mexico has significant geothermal potential. This short communication paper presents a machine learning (ML) methodology for curating and analyzing the PFA data from the DOE’s geothermal data repository. The proposed approach to identify potential geothermal sites in the Tularosa Basin is based on an unsupervised ML method called non-negative matrix factorization with custom k-means clustering. This methodology is available in our open-source ML framework, GeoThermalCloud (GTC). Using this GTC framework, we discover prospective geothermal locations and find key parameters defining these prospects. Our ML analysis found that these prospects are consistent with the existing Tularosa Basin’s PFA studies. This instills confidence in our GTC framework to accelerate geothermal exploration and resource development, which is generally time-consuming.

Optimal grid expansion under future electricity demand for groundwater irrigation in Ethiopia

Ethiopia is a lower-income country with an agrarian based, fast-growing economy that is susceptible to climate change impacts. As the nation continues to develop, domestic electricity consumption and electricity export opportunities will increase alongside a growing electricity demand for groundwater irrigation to mitigate the effects of drought. There are upcoming mega-dams– the Grand Ethiopian Renaissance Dam, Genale Dawa III, and Koysha – that will triple the electricity generation capacity. However, the central electricity grid is not sufficient to efficiently utilize this new capacity. We model future grid expansion scenarios by building a regional-scale linear optimal power flow model based on a transshipment representation of power flow that guides cost-optimal generation operations and transmission expansion, while satisfying competing electricity demand for domestic electricity consumption, groundwater irrigation, and international electricity export.We first evaluate cost-optimal generation and transmission expansion for a baseline case, with an estimate of current Ethiopian electricity demand. We then consider five potential future demand scenarios. These demand scenarios include estimates of both present and future domestic electricity demand, multiple levels of productive electricity demand for groundwater irrigation, and demand for international exports. Compared to the baseline, seasonal capacity factors for generators in the five model scenarios increased when collocated with an increase in electricity demand except for the generators that already operated at maximum capacity. All scenarios with higher domestic regional demand than the baseline surpass the transmission expansion planned by the Ethiopian Electric Power Corporation, independent of changes in irrigation and export demand. Our analysis suggests that the current expansion plan is not sufficient to meet future demand. Our research contributes to the literature by creating an optimal power flow model that can be applied in data-poor environments to model endogenous electricity infrastructure decisions under different scenarios of internal and external demand.

High-Reliability Load Recognition in Home Energy Management Systems

The increasing domestic demand for electricity has required the scientific community to develop new technologies focused on energy expenditure minimization. In this context, Home Energy Management System (HEMS) is an element that can incorporate applications that aid in efficient energy management, such as load recognition. Load recognition allows HEMS to map all appliances, assisting in efficient management. In this regard, methods that solve the load recognition problem are vital for HEMS. The motivation of this work is to propose a novel system that enhances load recognition in HEMS applications. For this purpose, the proposed system contains an event detector stage based on the Wavelet transform and another stage for appliance identification consisting of two processing chains, one for high reliability and one for fast model training. The main novelties of our work are: (i) very-low training time via fast training processing chain, less than 1 second with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -Nearest Neighbor (k-NN); (ii) the highest values for accuracy, F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , and the kappa index, 98.90%, 98.77%, and 0.9382, respectively, for the regularized Convolutional Neural Network (CNN-r) using the ‘Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology’ (REFIT) dataset; (iii) this is the first paper employing CNN-r and Vision Transformer (ViT) for load recognition problem; and (iv) the approach is applicable in households without any restrictions, as long as training the models. Regarding the tested scenarios, the results reveal a very efficient strategy yielding accuracy values greater than 98% for the REFIT dataset and the Reference Energy Disaggregation Dataset (REDD). Finally, as presented in Table 1, the proposed approach provides the highest performance compared to other methods available in the literature.

Optimal home energy management strategy: A reinforcement learning method with actor-critic using Kronecker-factored trust region

The global development trend of building decentralized and low-carbon energy systems has given rise to challenges in the demand side energy management. With the overwhelming increase in the domestic electricity demand and distributed energy penetration, scheduling residential energy consumption has become essential for decreasing environmental impact. To this end, the paper proposes an energy scheduling strategy optimization framework for a home energy management system devised with photovoltaic and storage to realize the optimal scheduling of household appliances. Uncertainties in energy usage behaviour and real-time electricity prices were considered and described using a Markov decision process model. Subsequently, to search for the optimal appliance scheduling policy in a complex and changing environment, a model-free energy scheduling approach was proposed based on actor-critic using the Kronecker-Factored Trust Region (ACKTR). Unlike conventional deep reinforcement learning methods, the proposed ACKTR-based approach has high sampling efficiency and can deal with both discrete and continuous control actions to jointly optimize the scheduling strategies of various types of appliances. Finally, the numerical results of case studies validate the effectiveness and demonstrate that the proposed approach can significantly reduce costs while maintaining satisfaction, with a remarkable 25.37% cost saving on a typical test day.

Energy Security of Hydropower Producing Countries—The Cases of Tajikistan and Kyrgyzstan

Energy security, as one of the most important components of state security, is a permanent element of academic debates and political discussions. Owing to the multidimensional and multifaceted nature of energy security, defining it is a complex process, requiring the consideration of a wide range of factors straddling economics, geology, ecology and geopolitics which decide whether we are dealing with the state of energy security or the lack of it. Energy security is usually equated with the security of supply. Another group of definitions of energy security focuses on the concept of security of services. A different approach to energy security issues is presented by energy exporting countries, whose objective is to ensure sufficiently high and stable income from sales of energy resource exports (security of demand). The subject of this paper is an analysis of the energy security of hydropower-producing countries—Tajikistan and Kyrgyzstan. Energy security has been analyzed in the context of security of supply, services, and demand on the basis of the approach proposed by Llamosas and Sovacool. So far, no work has been carried out to analyse the hydropower sectors of Kyrgyzstan and Tajikistan in the context of the energy security comparison of both countries. It is worth emphasising that their energy security and mutual relations are important from the point of view of the stability of the entire Central Asian region. Kyrgyzstan and Tajikistan have large hydropower potential, which, if properly used, could cover their domestic electricity demand and the surplus can be exported to neighbouring countries. Unfortunately, both countries are not utilising this potential for the time being. The main difficulty in the area of security of supply and services is the seasonality and low reliability of electricity supplies. Among the reasons for this are the poor technical and economic conditions of energy companies as a result of maintaining low tariffs, the irrecoverability of consumers’ energy bills, electricity theft, significant transmission loses and a high level of corruption. Although both countries aspire to the role of an energy exporters, they are themselves forced to import electricity from neighboring countries.

The Role of Clean Generation Technologies in the Energy Transformation in Poland

The ongoing transformation of the Polish power sector have been shaped by a number of factors. In this article, both external conditions for the Polish energy market—such as the country’s membership in the European Union—and internal ones—including domestic energy resources and demand for electricity, or the structure of the Polish economy—have been analysed. This study focuses specifically on the issue of development of clean generation technologies as these are fundamental to the energy-transition process. Its determinants have been examined empirically. To this end, econometric models have been developed using sectoral data from the Polish electric power industry. Improvements in technology have been measured as innovation inputs, that is, RD&amp;D investments in environmental technologies. Explanatory variables encompass economic as well as institutional measures: energy price, environmental policy stringency, long-term interest rate, market concentration and protection of intellectual property rights. The findings obtained point to a positive impact of the variables named on the innovative activity in Poland, with the exception of market concentration, which was proven to be statistically insignificant.

Assessing green energy growth in Nepal with a hydropower-hydrogen integrated power grid model

The involvement of green hydrogen in energy transformation is getting global attention. This assessment examines the hydrogen production and its utilization potential in one of the hydropower-rich regions, Nepal under various demand growth and technology intervention scenarios by developing a power grid model of 52 nodes and 68 transmission lines operating at an hourly time-step. The model incorporates a grid-connected hydrogen storage system as well as charging stations for electric and hydrogen vehicles. The least-costly pathways for power grid expansion at the nodal and provincial levels are identified through optimization. The results show that 32 GW of installed capacity is required to meet domestic electricity demand and 14 GW more hydropower should be exploited to completely decarbonize the transport sector by 2050. For maintaining 50% shares of hydrogen vehicle in the transport sector and meet government electricity export targets, Nepal requires 5.7 GW, 12 GW and 23 GW of the additional electrolyzer, hydrogen storage tanks and storage-based hydropower capacities respectively. For a given electricity demand, introducing hydrogen systems can reduce the capacity requirements of hydro storage by storing surplus power generated from pondage run-of-the-river and run-of-the-river hydropower during the rainy season and using it in the dry season.

ՀՀ էներգետիկ ենթակառուցվածքների զարգացման հեռանկարները. ռիսկեր և նոր հնարավորություններ/RA Energy Infrastructure Development Prospects։ Risks and New Opportunities

Today, in addition to meeting domestic demand for electricity, Armenia also exports electricity to neighboring countries. This fact indicates that the sector can be of vital and strategic importance for the development of the national economy and increasing the economic competitiveness of the country. Therefore, the further development of the sector and the more effective use of existing competitive opportunities can be the best preconditions for promoting the country's economic growth, increasing economic competitiveness and ensuring national security. The paper addresses the current trends in the modern energy system of the Republic of Armenia and the possible risks and challenges arising from them, as well as the motivations for the development of the sector by the public and private sectors.

Power-to-gas utilization in optimal sizing of hybrid power, water, and hydrogen microgrids with energy and gas storage

Electricity and water have constituted two main requirements of human societies. Water desalination is considered as a prominent source of drinking water for the future needs. Supplying desalination energy and domestic electricity demand is mainly from the grid integrated with the fossil-fuel-powered plants. Additionally, the hydrogen demanded by the industry is currently produced from fossil fuels by reforming. This vast reliance on fossil fuels means high cost and pollution, and also energy security risks. Using renewable resources is a promising way to overcome these problems. However, a portion of the generated power is inevitably curtailed due to various reasons. The Power-to-gas (P2G) is a promising solution to mitigate renewable curtailment mitigation. Accordingly, this paper presents a combined configuration for supplying electricity, water, and hydrogen in a microgrid system integrated with the P2G concept. The microgrid relies on renewable power sources and is supported by the diesel generator and battery storage. The complete P2G cycle, including hydrogen production, storage, consumption, and converting back to electricity by a fuel cell, is modeled and optimized. The hydrogen production is made by the water electrolyze in addition to the gas reforming. Results of the simulations indicate the superiority of P2G utilization.

A multi-energy system optimisation software for advanced process control using hypernetworks and a micro-service architecture

This paper describes a multi-energy system optimisation software, “Sustainable Energy Management System” (SEMS), developed as part of a Siemens, Greater London Authority and Royal Borough of Greenwich partnership in collaboration with the University of Nottingham, Nottingham Trent University and Imperial College London. The software was developed for application at a social housing estate in Greenwich, London, as part of the Borough’s efforts to retrofit the energy systems and building fabric of its housing stock. Its purpose is to balance energy across vectors and networks through day-ahead forecasting and optimisations that can be interpreted as control outputs for energy plant such as a water source heat pump, district heating pumps and values, power switchgear, gas boilers, a thermal store, electric vehicle chargers and a photovoltaic array. The optimisation objectives are to minimise greenhouse gas emissions and operational cost.The tool uses Hypernetwork Theory based orchestration coupled with a microservice architecture. The distributed nature of the design ensures flexibility and scalability. Currently, microservices have been programmed to forecast domestic heating demand, domestic electricity demand, electric vehicle demand, solar photovoltaic generation, ground temperature, and to run a day-ahead energy balance optimisation. This paper presents the results from both domestic heat and electricity demand forecasting, as well as the overall design and integration of the software with a physical system.The works build on that of O’Dwyer, et al. (2020) who developed a preliminary energy management software and digital twin. Their work acts as a foundation for this real-world commercialisation-ready program that integrates with physical assets.

The time-varying elasticity of South African electricity demand

This paper estimates the price and income elasticity coefficients of domestic electricity demand for the period 1980 to 2018 in South Africa. South African electricity prices were falling in real terms between 1983 and 2005. It then increased sharply in response to substantial tariff increases between 2008 and 2011. A time-varying parameter model with the Kalman filter is applied to estimate the evolution of the elasticities over time. This allows the analysis to distinguish between the two regimes of decreasing and increasing real electricity prices and evaluate the evolution of demand elasticities accordingly. The main result, consistent with existing South African literature, is that electricity consumption was unresponsive to price changes in the period of falling real electricity prices up to 2005. However, when real prices started increasing, the price elasticity coefficient increased markedly in absolute terms. The key result that policymakers should take note of is that aggregate price sensitivity is notably higher when real prices are increasing. While price elasticity estimates remain lower than unity (i.e. relatively inelastic), further price increases may incentivise consumers to substitute into alternative sources of energy. This result is echoed by findings from the comparable international literature.

DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany

Real-world domestic electricity demand datasets are the key enabler for developing and evaluating machine learning algorithms that facilitate the analysis of demand attribution and usage behavior. Breaking down the electricity demand of domestic households is seen as the key technology for intelligent smart-grid management systems that seek an equilibrium of electricity supply and demand. For the purpose of comparable research, we publish DEDDIAG, a domestic electricity demand dataset of individual appliances in Germany. The dataset contains recordings of 15 homes over a period of up to 3.5 years, wherein total 50 appliances have been recorded at a frequency of 1 Hz. Recorded appliances are of significance for load-shifting purposes such as dishwashers, washing machines and refrigerators. One home also includes three-phase mains readings that can be used for disaggregation tasks. Additionally, DEDDIAG contains manual ground truth event annotations for 14 appliances, that provide precise start and stop timestamps. Such annotations have not been published for any long-term electricity dataset we are aware of.

Synthetic Domestic Electricity Demand in Thailand using A Modified High Resolution Modelling Tool by CREST

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.