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This paper estimates a model of household-level demand for electricity services and electricity demand in the Indian state of Rajasthan using a combination of household-level survey and administrative data. The model incorporates customer-level demographic characteristics, billing cycle-level weather variables, and the fact that households face increasing block prices of electricity. The model allows estimating consumer response to price changes by four categories of energy services demand, namely, heating and cooling, lighting, and for domestic and business end-uses. The knowledge of demand response across different end-use helps in differentiating the impact of price changes along the income distribution. The model finds that the demand for heating and cooling energy is the most price inelastic and income elastic service, whereas the demand for domestic end-use is the most price elastic and income inelastic service of all four categories. The structural demand model also helps in comparing the welfare implications of current energy tariffs to those based on normative principles of efficient retail electricity pricing. For this analysis, first, the social marginal cost of electricity is calculated using publicly available data on generation, transmission, and distribution losses and emissions. The social marginal cost estimate, in combination with observable household characteristics, is then used to examine alternative tariff structures that are more affordable, equitable, and revenue sufficient for the utility than current price structure. An alternative tariff design, comprising of an energy price set to the social marginal cost of electricity and a fixed cost component determined by proxy indicators of household willingness to pay, performs better on the above parameters than the current schedule. Other sources of technical losses, related to transmission or distribution, are not studied in this paper.

Electric Power --- Electricity Demand --- Energy --- Energy and Poverty Alleviation --- Energy Demand --- Energy Policies and Economics --- Energy Price --- Energy Pricing --- Energy Utility

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Albania is among the most vulnerable countries to external energy shocks and climatic conditions, because of its high dependency on hydropower for electricity. Given highly volatile international energy prices and expected global warming, it is becoming increasingly important to manage the demand for electricity. However, the country has long been faced with a significant problem of electricity metering. About one-third of total energy is lost for technical and nontechnical reasons. This paper estimates the residential demand function by applying a two-stage system equation method for an endogenous censored variable, because the lack of metering makes the electricity consumption partially observable for the econometrician. It is found that metering is important to curb non-essential electricity use by households. The electricity demand could also be reduced by raising the first block rate and lowering the second block rate and the threshold between the two blocks. In addition, weather conditions and home appliance ownership would affect the demand for electricity. But the latter looks more influential than the former.

Climate change --- Climate Change Economics --- Climate Change Mitigation and Green House Gases --- Electricity demand --- Energy --- Energy and Environment --- Energy Production and Transportation --- Environment and Energy Efficiency --- Metering --- Nelson-Olsen two-stage estimator

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The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind

Research & information: general --- Technology: general issues --- deep learning --- energy demand --- temporal convolutional network --- time series forecasting --- time series --- forecasting --- exponential smoothing --- electricity demand --- residential building --- energy efficiency --- clustering --- decision tree --- time-series forecasting --- evolutionary computation --- neuroevolution --- photovoltaic power plant --- short-term forecasting --- data processing --- data filtration --- k-nearest neighbors --- regression --- autoregression --- deep learning --- energy demand --- temporal convolutional network --- time series forecasting --- time series --- forecasting --- exponential smoothing --- electricity demand --- residential building --- energy efficiency --- clustering --- decision tree --- time-series forecasting --- evolutionary computation --- neuroevolution --- photovoltaic power plant --- short-term forecasting --- data processing --- data filtration --- k-nearest neighbors --- regression --- autoregression

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The use of data collectors in energy systems is growing more and more. For example, smart sensors are now widely used in energy production and energy consumption systems. This implies that huge amounts of data are generated and need to be analyzed in order to extract useful insights from them. Such big data give rise to a number of opportunities and challenges for informed decision making. In recent years, researchers have been working very actively in order to come up with effective and powerful techniques in order to deal with the huge amount of data available. Such approaches can be used in the context of energy production and consumption considering the amount of data produced by all samples and measurements, as well as including many additional features. With them, automated machine learning methods for extracting relevant patterns, high-performance computing, or data visualization are being successfully applied to energy demand forecasting. In light of the above, this Special Issue collects the latest research on relevant topics, in particular in energy demand forecasts, and the use of advanced optimization methods and big data techniques. Here, by energy, we mean any kind of energy, e.g., electrical, solar, microwave, or wind

deep learning --- energy demand --- temporal convolutional network --- time series forecasting --- time series --- forecasting --- exponential smoothing --- electricity demand --- residential building --- energy efficiency --- clustering --- decision tree --- time-series forecasting --- evolutionary computation --- neuroevolution --- photovoltaic power plant --- short-term forecasting --- data processing --- data filtration --- k-nearest neighbors --- regression --- autoregression --- n/a

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Water is necessary to produce energy, and energy is required to pump, treat, and transport water. The energy–water nexus examines the interactions between these two inextricably linked elements. This Special Issue aims to explore a single "system of systems" for the integration of energy systems. This approach considers the relationships between electricity, thermal, and fuel systems; and data and information networks in order to ensure optimal integration and interoperability across the entire spectrum of the energy system. This framework for the integration of energy systems can be adapted to evaluate the interactions between energy and water. This Special Issue focuses on the analysis of water interactions with and dependencies on the dynamics of the electricity sector and the transport sector

History of engineering & technology --- waste heat recovery --- absorption cooling --- water–energy nexus --- steelworks --- TRNSYS --- non-equilibrium molecular dynamics --- deformed carbon nanotubes --- deformed boron nitride nanotubes --- water transport --- diffusion --- Z-distortion --- XY-distortion --- screw distortion --- oil/water separation --- superhydrophilic/underwater-superoleophobic membranes --- opposite properties --- superhydrophobicity/superoleophilicity --- selective wettability --- micro/nanoscale composite structure --- virtual water network --- inter-provincial electricity transmission --- structural decomposition analysis --- electricity-water nexus --- cooling tower --- response surface model --- water --- power plant --- decarbonization --- energy concepts --- long-term energy storage --- power-to-gas --- power-to-X --- wastewater treatment --- anaerobic digestion --- water-energy nexus --- demand response --- energy consumption optimization --- multi-objective model --- urban water system --- local water supply --- electricity demand --- index decomposition analysis