Analysis Of Electricity Demand Research Articles

A Multitask Integrated Deep-Learning Probabilistic Prediction for Load Forecasting

Spinning reserve without accurate load forecasting can lead to automatic disconnection of critical loads by under-frequency load shedding devices. Such a predicament poses a grave threat to the economic and social welfare of the affected region, and in extreme scenarios, can result in a debilitating grid collapse. However, existing models lack the deep feature extraction capability and cannot accurately predict the uncertainty of electricity demand. Thus, a multitask integrated deep-learning probabilistic prediction with multidimensional feature extraction based on granularity information and quantile regression is explored in this study to solve the insufficient feature extraction problem in probabilistic prediction. In the proposed scheme, we designed a multi-layer feature extraction framework, which information granularity based on fuzzy rough sets to extract time-domain features, multilayer Laplace operators to extract features globally, and quantile regression recurrent neural network variants to extract time context information. In addition, the Pareto integration method is extended to capture better individuals. The experimental results demonstrate that the proposed system can effectively improve deterministic forecasting and uncertainty analysis of electricity demand and manage daily fluctuations.

Data-driven classification of Urban Energy Units for district-level heating and electricity demand analysis

The building sector is a significant contributor to global energy consumption and accounts for approximately one-third of total greenhouse gas emissions. While building energy analysis has traditionally focused on individual buildings, analyzing larger settlements, such as districts or neighbors, offers additional opportunities. The objective of this study is to define and classify typical urban areas for energy analysis, referred to in this paper as Urban Energy Units (UEUs), which represent geographical regions within a city with specific building’s characteristics, settlement patterns and energy demand. Sixteen different UEUs were classified using literature and open data. The proposed methodology leverages open-source data and uses a random forest model to enhance missing building properties of the building stock such as building age and construction type. It further subdivides the study area into geographically defined sections, and deploys a decision tree model to classify these sections into the sixteen different UEUs. These UEUs enable the creation of energy districts in a modular manner and flexible for its use in any given area. This study demonstrates the practical implications related to the 2023 german municipality heating plan. The methodology was applied in Oldenburg, a mid-sized German city. The city was subdivided into a total of 8249 UEUs, with the detailed results for energy demand presented in this report.

Simulation Analysis of Electricity Demand and Supply in Japanese Communities Focusing on Solar PV, Battery Storage, and Electricity Trading

This study analyzes how the electricity demand and supply constitutions affect electricity independence and power trading within a community and between a community and a grid through simulation analysis. To that aim, we create a simulation model equipped with a community-building function and trading capability. We first construct a community consisting of various types of residential and industrial consumers, and renewable power plants deployed in the community. Residential and industrial consumers are characterized by a state of family/business and ownership and the use of energy equipment such as rooftop solar PV and stationary battery storage in their homes/offices. Consumers’ electricity demand is estimated from regression analyses using training data. Using the hypothetical community constructed for the analysis, the simulation model performs rule-based electricity trading and provides outputs comprising the total electricity demand in the community, the state of use of battery storage and solar PV, the trading volume, and the electricity independence rate of the community. From the simulation results, we discuss policy implications on the effective use of renewable energy and increasing electricity independence by fully utilizing battery and trading functions in a community.

Analysis and Sizing of Charging Stations in Kota City

This paper focuses on optimization of charging station capacities and locations. The proliferation of electric vehicles is inextricably linked to the deployment of charging infrastructure. Currently, there are issues with locating and sizing when it comes to the building of electric vehicle charging stations. In order to find out the capacity of charging stations, a mathematical model was developed, which includes a site survey, EV density, electricity demand analysis, load modeling, costing analysis, EV battery charging time and power quality. The proposed approach was tested in MATLAB/Simulink environment.

Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm

Mid-term electricity demand forecasting plays an important role in ensuring the operational safety of the power system and the economic efficiency of grid companies. Most studies have focused on deterministic forecasting of electricity demand, while ignoring uncertainty analysis of electricity demand. To bridge this research gap, a point-interval mid-term electricity demand forecasting model is proposed. Firstly, based on Pearson correlation coefficient, feature dimensionality reduction is carried out to filter out key features that greatly affect electricity demand, such as socio-economic factors and meteorological factors, to enhance forecasting efficiency. Secondly, improved variational mode decomposition (IVMD) optimized by sparrow search algorithm (SSA) is proposed to decompose electricity demand series into several subsequences. By combining SSA, extreme learning machine (ELM) and adaptive boosting algorithm (Adaboost), IELM-Adaboost is constructed to forecast each subsequence, and the point forecasting results are obtained by superimposing each subsequence forecasting result. Finally, electricity demand forecasting intervals will be obtained by the application of the kernel density estimation (KDE) of point forecasting error. Three Chinese provinces are applied for empirical analysis in this paper. Compared with ELM, the MAPE of the proposed model in the three datasets are reduced by 87.90%, 66.05% and 75.97% respectively, showing promising point forecasting performance. The empirical results prove that IVMD-IELM-Adaboost performs well in both point forecasting and interval forecasting.

Lessons from residential electricity demand analysis on the time of use pricing experiment in South Korea

This study analyzes a large-scale Time of Use (TOU) pricing experiment, the first attempt in South Korea, where a single standard rate under increasing block pricing is applied to all residential customers. The experiment was conducted for 2033 households living in 16 apartment complexes, where the control group was subject to the current standard rate, while the two treatment groups had different rate schedules under TOU pricing. Using the data that included both the pre-experimental period (September 2018–September 2019) and post-experimental period (October 2019–August 2020), we built on a two-level electricity demand model to analyze (1) if TOU pricing effectively transfers the peak demand of residential customers within a day, and (2) the changes in daily electricity consumption. We found that the households effectively reduced the peak demand in response to relatively high prices. However, unlike the control group, whose price responsiveness did not change over the sample period, that of the treatment groups decreased in the post-experimental period, thus resulting in increased intermediate consumption. The reason is the reduced price index resulted from the effective load transfer of households, combined with low intermediate price that does not reduce consumption. The price schedule under TOU pricing should be reexamined for social welfare.

Lessons from Residential Electricity Demand Analysis on the Time of Use Pricing Experiment in South Korea

Lessons from Residential Electricity Demand Analysis on the Time of Use Pricing Experiment in South Korea

Can electricity demand lead to air pollution? A spatio-temporal analysis of electricity demand with climatic conditions

India, the third largest electricity producer, has been suffering from high power deficit for the past few decades. In recent years, the Government of India (GOI) has taken several initiatives to provide uninterrupted energy availability to citizens. The proposed study is aimed to help Indian government and private agencies in proactive planning of various energy related activities including spatial resolved generation planning, timestamp activities planning, transmission network optimization and many more. In this context, the article delivers details about the performed Spatio-temporal analysis of energy availability/deficiency in the country, India. Furthermore, as a step towards overall electrification, the Indian government had launched the Saubhagya scheme (2017) to provide reliable and accelerated electricity access to un-electrified rural and urban households. The analytical assessment of citizens' views on such policies is beneficial for the government agencies to decide future spatial movement and resources planning. Hence, this study, later on, delivers a novel insight in the energy domain by examining citizens' views through Twitter-based regional analysis. This analysis gives inference regarding citizens’ sentiments on policies or electrification status published by GOI. Here, the presented work showed that citizens from four out of the five regions express positive opinions on electrification progress made by GOI. In the last stage, the impact of increasing electricity demand and climate changes on air pollution of a city is examined using the Granger causality test. The test experiments are performed on the well-planned northern city, Chandigarh. Although many researchers have explored the association between the climatic conditions and the energy demand, in this work, along with these factors, the causal interaction with air pollution is also shown.

A network analysis of electricity demand and the cryptocurrency markets

AbstractThis article examines the connectedness and information spillover in the Electricity‐Crypto Network (ECN) system. The Bitcoin and Ethereum markets are studied due to the level of electricity demand for active trading and mining in the three leading crypto mining economies (United States, China, and Japan). Among other findings, the leading net transmitter of information is the return of the Bitcoin market while the demand for electricity in the U.S. and Japan are the leading net information receivers in the ECN system. In a nutshell, the return and trading volumes of the cryptocurrency markets are net information transmitters while the markets' volatility and the demand for electricity in the U.S., China, and Japan are net information receivers in the system. As a policy relevance, given the favourable developments in these crypto markets, greener sources of electrical energy are expedient to mitigate emissions while mining these coins. This will reduce the impact of human activities on the climate.

Research on the method of electricity demand analysis and forecasting: the case of China

With economic expansion of China having moderated to a "New Normal" phase, concerns on the surplus supply of electricity have accelerated, especially in the northwest and northeast regions. The ongoing power system reform also brings various of uncertainties, posing challenge to power supply and demand balance. Therefore, the accurate estimation of electricity demand is still inevitable and urgent. In this paper, the causality relationship of electricity demand and the selected factors, namely GDP, population, energy structure, industrial structure and urbanization, is examined by using Stationary, Co-integration and Granger Causality Tests. Then the direct and indirect effects of these factors are investigated via a path-coefficient analysis. Finally, in these foundations, an improved Chicken Swarm Optimization based on Stimulated Annealing, namely Stimulated Annealing Chicken Swarm Optimization, is proposed to optimize the weighting factors of three forms of electricity demand models. The Stimulated Annealing Chicken Swarm Optimization not only inherits the advantages of the standard Chicken Swarm Optimization such as uncomplicated principle, handy implementation and robustness to control parameters, but also can avoid premature convergence to improve the ability of finding the best solution. Case study reveals that the Stimulated Annealing Chicken Swarm Optimization has better predictive ability than other benchmark algorithms.

Incorporating the influence of China's industrial capacity elimination policies in electricity demand forecasting

Elimination of high-emitting industrial capacity can be an effective way to control and relieve air pollution but it will also influence demand. Accurate forecasting of electricity demand requires consideration of the impact of capacity elimination policies. This paper applies a modified firefly algorithm improved by Gaussian disturbance to optimize the parameters of Support Vector Machine (SVM) in order to quantify the impact of capacity elimination policies. We consider three policy scenarios and three growth scenarios in our analysis. The results demonstrate that modified firefly algorithm (MFA) can improve the forecasting performance of SVM and electricity demand forecasting considering the influence of capacity elimination policies, and provide good reference for policy analysis and electricity demand analysis.

Multifractal analysis of electricity demand as a tool for spatial forecasting

Electrical utilities need to plan their investments in substations and networks to meet future customer demand, by predicting the spatial load growth and its time trend. Several techniques are currently in use to do that, such as trending analysis or simulation methods. To study the electricity demand we used multifractal analysis. A fractal is an object whose irregularities are not smooth and have some self-similarity at different scales. If the fractal does not have strict self-similarity, we could break such fractality, if it really exists in the system, in a spectrum of sub fractals which have a self-similar structure, performing the so-called multifractal spectral analysis. Multifractal spectral analysis has been already applied to study the morphology and population growth of cities. Because electricity demand can be related to demographics of cities, it is possible to consider the hypothesis that multifractal spectral decomposition can be applied to analyze electricity demand. A variety of multifractal analyses were performed on real data from the customer demand of an electrical utility. The results show that the analyzed electricity demand is split into clear and interesting two-multifractal distribution with properties not found yet in the literature on the subject. This type of multifractal analysis could lead the way to improved spatial demand forecasting methods.

Suburban Housing Development and Off-Grid Electric Power Supply Assessment for North-Central Nigeria

Energy infrastructures in North-Central Nigeria are inadequate and grid electricity is unable to meet suburban housing electricity demand. The alternative power-supply options proposed by government for the region require appropriation analysis for selection. Four public housing estates in suburban Abuja are selected for electricity demand analysis under conventional and energy-efficient lighting scenarios; then techno-economic parameters of two off-grid electric power supply systems (PV and Diesel-powered generation) to meet these electricity demands are evaluated. An energy techno-economic assessment methodology is used. The study determines the energy-efficient lighting system is appropriate with 40% energy savings relative to the Conventional Lighting Systems. The diesel generator alternative power-supply option has Life Cycle Costs almost 4 times those of the PV option. The study established the PV-energy-efficient lighting system as the most feasible off-grid electric power supply alternative for implementation.

Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand

We introduce a panel model with a nonparametric functional coefficient of multiple arguments. The coefficient is a function both of time, allowing temporal changes in an otherwise linear model, and of the regressor itself, allowing nonlinearity. In contrast to a time series model, the effects of the two arguments can be identified using a panel model. We apply the model to the relationship between real GDP and electricity consumption. Our results suggest that the corresponding elasticities have decreased over time in developed countries, but that this decrease cannot be entirely explained by changes in GDP itself or by sectoral shifts.

Functional-coefficient cointegration models in the presence of deterministic trends

ABSTRACTIn this article, we extend the functional-coefficient cointegration model (FCCM) to the cases in which nonstationary regressors contain both stochastic and deterministic trends. A nondegenerate distributional theory on the local linear (LL) regression smoother of the FCCM is explored. It is demonstrated that even when integrated regressors are endogenous, the limiting distribution is the same as if they were exogenous. Finite-sample performance of the LL estimator is investigated via Monte Carlo simulations in comparison with an alternative estimation method. As an application of the FCCM, electricity demand analysis in Illinois is considered.

A panel data analysis of electricity demand in the Pakistani industrial sector

ABSTRACTThe analysis uses data taken from the World Bank Enterprise Surveys for Pakistan to construct a panel dataset for 402 firms over 4 years. This is used to determine the elasticity of electricity demand with respect to the price of electricity across Pakistani firms. The electricity demand analysis finds that the price elasticity of electricity demand across the sample of firms is −0.57.

Extreme value analysis of electricity demand in the UK

For the first time, an extreme value analysis of electricity demand in the UK is provided. The analysis is based on the generalized Pareto distribution. Its parameters are allowed to vary linearly and sinusoidally with respect to time to capture patterns in the electricity demand data. The models are shown to give reasonable fits. Some useful predictions are given for the value at risk of the returns of electricity demand.

Electric Demand Analysis of the Tunisian Network: Trends and Short Term Photovoltaic Implementation with Alternatives Prospects and Technologies

The main objective of this study is to analyze the system of electric demand in Tunisia and to propose immediate alternative solution of photovoltaic implementation that can be suitable based on available natural solar energy resources in the country to respond to the short Forecast peak demand growth and energy consumptions taking into account financial strain. On the first step, analysis of the situation based on the recent historical data is proposed. In a second step, a solution is considered, based on photovoltaic implementation and using the results of historical data analysis. The resulting benefits are highlighted: financial, technical, environmental and social. Future studies may be made to use a mix of technology and policy as well: among it combining photovoltaic and battery storage, in parallel with energy efficiency programs.

A parsimonious diffusion equation for electricity demand

We present a parsimonious model for describing the stochastic dynamics of electricity demand in the nsw region of the National Electricity Market. We apply a moment matching approach to calibrate the parameters and perform in-sample and out-of-sample tests to demonstrate the model's capability and weaknesses. We show a solid improvement when the calibration uses the minimum and maximum daily temperatures in the regression. We clearly express the relationship between the drift term and the expected demand, which is a nontrivial connection and has not been made explicit in other publications. References An Introduction to Australia's National Electricity Market, July 2010, Australian Energy Market Operator Limited, Accessed from http://www.aemo.com.au/corporate/0000-0262.pdf March 2011. J. Alcock, J. Goard and T. Vassallo, Calibrating Mean Reverting Jump Diffusion Models: An Application to the nsw Electricity Market, in T. Marchant Ed. Proceedings of the 2007 Mathematics and Statistics in Industry Study Group, MISG, Wollongong, 57–81, 2007. http://www.uow.edu.au/content/groups/public/@web/@inf/@math/documents/doc/uow040974.pdf C. Blanco and D. Soronow, Mean Reverting Processes–-Energy Price Processes Used for Derivatives Pricing and Risk Management, Commodities Now, June, 68–72, 2001. http://www.fea.com/resources/a_mean_reverting_processes.pdf S. R. Brubacher and G. Tunnicliffe Wilson, Interpolating Time Series with Application to the Estimation of Holiday Effects on Electricity Demand, J. Royal Statistical Society Series C, 25:107–116, 1976. http://www.jstor.org/stable/2346678 N. R. Draper, and H. Smith, Applied Regression Analysis, Wiley Series in Probability and Statistics, 1998. R. F. Engle, C. Mustafa, J. Rice, Modelling peak electricity demand, Journal of Forecasting, 11:241–251, 1992. doi:10.1002/for.3980110306 E. Erdogdu, Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey, Energy Policy, 35:1129–1146, 2007. doi:10.1016/j.enpol.2006.02.013 F. Li, Singular value decomposition expansion for electrical demand analysis, IMA Journal of Mathematics Applied in Business and Industry, 11:37–48, 2000. doi:10.1093/imaman/11.1.37 H. Geman, Commodities and Commodity Derivatives, Wiley, Chichester, 2005. J. M. Gourd and N. Hansen, Comparison of the performance of a time-dependent short-interest rate model with time-dependent models, Applied Mathematical Finance, 11:147–164, 2004. doi:10.1080/13504860410001686034 S. Mirasgedis, Y. Sarafidis, E. Georgopoulou, D. P. Lalas, M. Moschovits, F. Karagiannis and D. Papakonstantinou, Models for mid-term electricity demand forecasting incorporating weather influences, Energy, 31:208–227, 2006. doi:10.1016/j.energy.2005.02.016 B. Oksendal, Stochastic Differential Equations: An Introduction with Applications, 4th ed., Springer-Verlag, New York, NY, 1995. B. R. Szkuta, L. A. Sanabria, T. S. Dillon, Electricity price short-term forecasting using artificial networks, IEEE Trans Power Systems, 14:851–857, 1999. doi:10.1109/59.780895 B. Petschel, Mean Reversion Models for Weather Derivatives, PhD Thesis, University of Queensland, 2005 J. W. Taylor, Short-term electricity demand forecasting using double seasonal exponential smoothing, Journal Operational Research Society, 54:799–805, 2003. doi:10.1057/palgrave.jors.2601589 S. C. Tripathy, Demand Forecasting in a Power System, Energy Convers. Mgmt. 38:1475–1481, 1997. doi:10.1016/S0196-8904(96)00101-X R. Weron and A. Misiorek, Modeling and Forecasting Electricity Loads: A Comparison, in Proceedings of The European Electricity Market EEM-04 Lodz, Poland, 135–142, 2004. C. J. Ziser, Z. Y. Dong, T. Saha, Investigation of Weather Dependency and Load Diversity on Queensland Electricity Demand, in M. Negnevitsky Ed. Proceedings of Australasian Universities Power Engineering Conference 2005, AUPEC, Hobart, 2:457–462, 2005.

An Econometric Analysis of Electricity Demand for the Residential Sector of Pakistan

An Econometric Analysis of Electricity Demand for the Residential Sector of Pakistan