Electricity Consumption Model Research Articles

Optimization of peak-valley pricing policy based on a residential electricity demand model

In order to deal with the rapid growth in residential electricity consumption, residential peak-valley pricing (PVP) policies have been implemented in 12 provinces in China. However, being inappropriate, the residential PVP policies have delivered no significant results. The challenge to China's PVP policy research lies in obtaining high-resolution electricity consumption data and modeling residents' behavior. By simulating household electricity load profiles, an electricity price policy response model and a residential PVP policy optimization model, are constructed and applied in this paper to simulate the effect of the current residential PVP policy and optimize the policy. The results show that the peak-shaving capability (represented by the reduction in peak load) of the PVP policy in 11 provinces is less than 3%, while the PVP policy in Gansu Province has increased household electricity bills by about 5.7%. In addition, the optimized PVP can reduce household electricity bills by 3% and reduce peak electricity consumption by about 9%. The 12 provinces should adopt the 3-phase division method and optimize the electricity price in the peak and valley (i.e. off-peak) periods respectively. This paper promotes the research on China's residential PVP policy and provides an effective reference for the design of the PVP policy.

Research on a Charging Mechanism of Electric Vehicles for Photovoltaic Nearby Consumption Strategy

With the promotion of the pilot development of distributed whole county roof photovoltaics in China, problems such as power consumption, energy regional balance, and grid stability have become prominent. In this paper, an application mode of electric vehicle (EV) charging network and distributed photovoltaic power generation local consumption is studied. The management idea of two-layer and four model has been established, including the regional distributed photovoltaic output model, electricity consumption model, EV consumption model, and regional grid load dispatching model, which can realize the scheduling of the energy flow formed by photovoltaic, induce the charging of EVs, and make the photovoltaic consumption in office building areas and residential building areas complementary. Firstly, according to the randomness of photovoltaic power generation and EV charging, the dynamic response capability, power support capability, effective convergence time, system stability, system failure rate, and other characteristics of regional loads are comprehensively analyzed, and the grid energy management model of EV charging network and distributed photovoltaic is proposed. Secondly, according to certain statistical characteristics, the distributed photovoltaic will be concentrated, and EV charging will be prioritized to achieve nearby consumption. Finally, different scenarios are described, and the scenarios of charging in the park, community life, and power supply service are selected for analysis. This mode is intended to guide the consumption of new energy through economic leverage, which can realize the unified regulation of distributed energy convergence, consumption and storage.

Machine learning based demand response scheme for IoT enabled PV integrated smart building

The short-term forecasting of electric power consumption and renewable energy generation with high efficiency and advanced demand side management is essential for improving the energy flexibility provided by the smart buildings. However, the recent literature fails to present the mechanism for developing demand side management for energy flexibility. This article addresses the importance of developing an electric power consumption and renewable energy generation short-term forecasting model using a supervised machine learning approach. In this work, random forest and decision tree regression models are developed and used to forecast future electric power consumption and renewable energy generation. To validate the forecasting model performance using the MAE, RMSE, MAPE, and R2. This article proposes a day ahead dynamic pricing model for the demand response scheme. The day ahead dynamic pricing minimizes the peak time demand and end-user electricity tariff. A smart grid system must know the future short-term electric power consumption and renewable energy generation to enable the demand response scheme. The educational institute smart substation and weather station are integrated into the internet of things devices to enable real-time monitoring and control of the electric power consumption and renewable energy generation. The results show that the proposed DR scheme benefits end electricity users and smart grid operators.

Electric-Vehicle Routing Planning Based on the Law of Electric Energy Consumption

In this paper, we establish the Electric Vehicle Routing Problem with Time Windows Based on Driving Cycles (EVRPTW-DC) to optimize the delivery routing of electric vehicles (EVs). As energy consumption may affect the maximal driving range and the recharging behavior of EVs, we first develop a nonlinear electric energy consumption model based on typical driving cycles of suburban and urban areas, with consideration of vehicle load, travel distance, and speed. An adaptive particle swarm optimization algorithm is then designed to solve the problem. Moreover, we study cases built from the actual operational data of Company J and compare the optimal delivery schemes of EVRPTW-DC and EVRPTW under the traditional linear electric energy consumption law. The results show that our nonlinear energy consumption model, which provides a better simulation of energy consumption, can lead to a more realistic delivery plan. Finally, we explore the applicability of the proposed EVPRTW-DC and discuss the conditions of using a linear electric energy consumption coefficient.

Assessing the causality relationship and time series model for electricity consumption per capita and human development in Colombia

Increasing access to electricity is essential to improve human development in poor and developing countries. To this end, the relationship between electricity consumption and human development is critical to supporting the process of decision-making. However, the discussion on this topic is limited. In Colombia, developing adequate policies requires characterizing the impact of electricity consumption on human development. This study uses time series and the Human Development Index data and per capita electricity consumption between 1990 and 2018 to assess their relationship. Furthermore, the government projections for electricity consumption and population growth are used to forecast the Human Development Index up to 2030. To this end, the econometric and correlation approach is applied. The Lagrangian multiplier unit root was used to verify if variables are stationary, while cointegration tests were used to define if non-stationary variables are cointegrated. Moreover, the Granger causality test was used to assess the causal relationships between the variables, and the ARIMA model was applied for the correlation analysis. Results show a bidirectional relationship between the Human Development Index and the per capita electricity consumption. Moreover, the government’s projection of per capita electricity consumption is forecasted to have little impact on the Human Development Index up to 2030. Therefore, developing adequate policies to improve access to electricity while contributing to human development is essential. Particularly for low-income citizens and rural areas, especially in non-interconnected, vulnerable, and geographically marginalized communities. However, further discussion is needed to characterize energy inequality through income classes, departments, and economic sectors, which is one challenge for the government to meet sustainable development goals.

Power Consumption Forecast of Three Major Industries in China Based on Fractional Grey Model

As one of the most significant carbon emission departments in China, the power industry will gradually become the core hub of reducing carbon emissions in the process of undertaking carbon emissions transferred from other industries. Therefore, it is of vital importance to predict the power consumption in China’s end energy consumption to achieve the carbon peak goal on time. This paper firstly uses the gray relational analysis model to study the relationship between power consumption indicators of the three major industries and some social and economic indicators and obtains the influencing factors with the greatest correlation with the power consumption of the three industries. Then, based on the analysis of socio-economic factors, considering different growth rates, the GMCN(1,N) model of electricity consumption in China’s three major industries is established. Forecast data under different scenarios have important practical significance for formulating active and effective energy policies. The data indicate that the secondary and tertiary industries consume the greatest amount of electricity. It is estimated that the power consumption of China’s three major industries will reach 10.15 trillion kWh (kilowatt hours) by 2030.

An Overview of Electricity in Cameroon: Current Status, Influential Factors and Government Actions

The mastery of demand for electricity in Cameroon is one of the concerns of the State, which is part of the development plan for the electricity sector by 2025. Thus, this paper identifies the factors that influence the electricity sector and mentions positive government actions. We use in this work data on the basis of previous work on one hand and a survey on the other hand in order to reinforce the analysis. The survey was conducted on a sample of 3,000 households in Douala in order to identify socio-economic factors that influence the electricity sector. For this, face-to-face approach was chosen. Households were randomly selected, then a questionnaire was submitted to them, assisting them in their response options. Results show that though certain factors have a positive influence on the electricity sector, Cameroon's current electricity system still remains unsustainable. A comprehensive view of how various factors influence the electricity sector in Cameroon would help in understanding the challenges for the future development of the sector. Government policies in this area would be more enlightened and undergo reorganization. Different models of electricity consumption could thus be formulated and adopted in order to predict the potential impacts of changes in planning.

Model of monthly electricity consumption of healthcare buildings based on climatological variables using PCA and linear regression

At this time, due to the global pandemic that has occurred, public administrations want to optimize resources and reduce greenhouse gases with more interest than before. It is the case of the Energy Regional Entity of the Junta de Castilla y León (Spain) that pursues the optimization of the energy consumption in particular of healthcare sector buildings. For this purpose, this work focuses on estimating electricity consumption for each month, for which different scenarios will be generated and the corresponding model is obtained for each scenario. This model has been developed considering the historical monthly data of consumption and climatic variables for the last 3 years. Electricity consumption in public sanitary buildings is related to their climatology, due to the use of air conditioning to adjust the indoor temperature. Subsequently, from the models obtained, the results will be analyzed. Significant differences have been observed in the estimation of electricity consumption with respect to the real data provided by the Junta de Castilla y León. The results obtained show how the availability of climatic variables increases the accuracy of the model obtained by about 30%.

ML-based energy management of water pumping systems for the application of peak shaving in small-scale islands

This study introduces an energy management method that smooths electricity consumption and shaves peaks by scheduling the operating hours of water pumping stations in a smart fashion. Machine learning models are first used to accurately forecast the electricity consumed and produced by renewable energy sources on an hourly level. Then, the forecasts are exploited by an algorithm that optimally allocates the operating hours of the pumps with the objective to minimize predicted peaks. Constraints related with the operation of the pumps are also considered. The performance of the proposed method is evaluated considering the case of a Greek remote island, Tilos. The island involves an energy management system that facilitates the monitoring and control of local water pumping stations that support residential water supply and irrigation. Results indicate that smart scheduling of water pumps in a small-scale island environment can reduce the daily and weekly deviation of electricity consumption by more than 15% at no monetary cost. It is also concluded that the potential gains of the proposed approach are strongly connected with the amount of load that can be shifted each day, the accuracy of the forecasts used, and the amount of electricity produced by renewable energy sources. • Electricity peak shaving in small scale islands. • Smart scheduling the operation of water pumping systems. • Exploiting ensembles of forecasting models for PV production and electricity consumption. • A heuristic optimization algorithm to support load shifting decisions.

Uni-Variate and Multi-Variate Short-Term Household Electricity Consumption Prediction Using Machine Learning Technique

Background: Electricity consumption prediction plays an important role in conservation, development, and future planning. Accurate prediction model has various field applications in real-life scenarios, future electricity demand estimation, performance evaluation of current time, fault detection, efficient energy production, resource-saving, and many more. In this paper, a CNN based short term building electricity consumption prediction model is developed and tested for two different types of datasets that can perform weekly prediction. Two different datasets are used to check how the algorithm behaves on different datasets i.e., what are the impacts dataset has on prediction accuracy. Errors were calculated using MAE and RMSE. Objective: The objective of the study is to develop an electricity consumption prediction (ECP) model for a univariate and multivariate dataset using CNN and LSTM network and to find that how the correlation and independency of features affect the electricity prediction task. Methods: The proposed electricity consumption model is built using the deep CNN andLSTM network and is trained and tested using the univariate and multivariate time series dataset thus the two experiments have been performed and are named as U-ECPCL (Univariate- Electricity Consumption Prediction using CNN and LSTM) and M-ECPCL (Multivariate- Electricity Consumption Prediction using CNN and LSTM) respectively. Results: The model predicts accurately with few errors with MAE of 0.251 and RMSE of 0.66 for univariate dataset and MAE of 4.36 and RMSE of 11.53 for a multivariate dataset. Conclusion: The model predicts accurately with few errors and if the prediction error of univariate and multivariate are compared then it is concluded that the univariate model outperforms the multivariate model.

Combined models of electricity consumption

The article investigates models and methods of electric load forecasting. It is shown that among the known methods of power consumption management, preference is given to those based on the use of forecast estimates. The analysis of works devoted to the issues of forecasting the processes of power consumption management systems of industrial enterprises is carried out. It is shown that it is expedient to use adaptive models as a basis for operative forecasting of loads of power supply systems of industrial enterprises. Analysis of adaptive models of electricity consumption forecasting based on the method of exponential smoothing showed their high efficiency and good adaptability to changes in the process of electricity consumption. It is shown that the greatest difficulty in forecasting are cases of abrupt changes in the development of the process. Abrupt changes in the process can lead to a violation of pre-existing qualitative relationships of the parameters of the projected system. If the jump is the transition of the predicted system from one steady state to another, the model of exponential smoothing with correction of the constant smoothing has the best adaptability to this kind of change. At the same time, changes of the "pulse" type are worked out by the model with a certain delay, which leads to an increase in the standard error of the forecast. Therefore, the model's response to change slows down. To eliminate this circumstance, a forecasting procedure based on combined models is proposed. The paper considers two models of combined forecasting - a combined model of joint processing of forecasting results and a combined model of selective type. Experimental studies of the considered models are carried out.

A novel two-stage seasonal grey model for residential electricity consumption forecasting

Accurate electricity consumption forecasting plays a significant role in power production and supply and power dispatching. Thus, a new hybrid model combing a grey model with fractional order accumulation, called FGM (1, 1), with seasonal factors, sine cosine algorithm (SCA), and an error correction strategy is proposed in this research. To accurately predict the seasonal fluctuations, seasonal factors are used in this model; Then, with the aim of improving the prediction performance, a SFGM (1, 1) model optimized by SCA rather than least square method, namely SCA-SFGM (1, 1), is establish to forecast electricity consumption; Moreover, considering forecasting error sequence may contain useful information, an error correction strategy is introduced to model forecasting error time series to adjust the preliminary forecasts of SCA-SFGM (1, 1). Fourth, four comparison models, three measurement criteria and a statistical hypothesis testing method using monthly residential electricity consumption dataset from 2015 to 2020 are designed to verify the prediction performance of models; Lastly, experimental results show that the mean absolute percentage error (MAPE) of the proposed model is 4.1698%, which is much lower than 14.5642%, 6.5108%, 5.9472%, 5.7060% and 4.9219% of GM (1, 1), SARIMA, SGM (1, 1), SFGM (1, 1) and SCA-SFGM (1, 1) models, respectively, showing that the proposed model can not only effectively capture seasonal fluctuations, it also adds an operational candidate forecasting benchmark model in electricity markets.

Electricity production and consumption modeling through fuzzy logic

This paper proposes a prediction model based on fuzzy logic applied to anticipate electricity production and consumption in a building equipped with photovoltaics and connected to the grid. The goal is a smart energy management system able to make decisions and to adapt the consumption to the actual context and to the future electricity levels. The interest is to use as much electricity as possible from own production. The surplus is captured by an energy storage system or is sent to the grid. When no electricity is available from self-production, the grid is used to cover the necessities. The evaluations are performed on a data set collected in a real household. The proposed method is compared in terms of mean absolute error with other existing methods. The method developed based on fuzzy logic has an error of about 67 W, which places it among the most efficient models.

Analysis of methods of electricity consumption forecasting for a coal industry enterprise

The paper considers a forecast model of electricity consumption of a coal industry enterprise based on three forecast methods, namely the wavelet transform, the vector method, and the recurrent neutral network. For preprocessing the data for forecasting by vector and recurrent methods, the Singular Spectrum Analysis method was chosen. The structure of the model allows taking into account individual features of the operating cycle of the production process and smoothing the noise components and outliers caused by these features. The results of a short-term hourly forecast for one day ahead are presented with the comparison of the obtained values. The results of short-term electricity consumption forecast were verified based on the actual data of the coal industry enterprise in order to assess the adequacy of the model to the actual values.

Mathematical modeling of power consumption based on rank analysis

In the article, based on the application of the rank analysis of the technocenosis, mathematical modeling of electricity consumption by consumers for the production of hard alloys is carried out. Two methodological levels of research in the field of energy-saving and power consumption are presented. At the same time, it was found that rank analysis allows you to streamline information, effectively predict power consumption by individual objects to identify dynamics, and visually represent objects with abnormal power consumption. The analysis of the results obtained confirms the efficiency of using the H-distribution for obtaining mathematical models of power consumption of Noah and Pointer castes in the production of hard alloys. For the first time, the regularity of changes in electricity consumption by technological equipment was established, which is described by an adequate mathematical model in the form of a damped harmonic function. The resulting model is recommended for use in an automated dispatching control system for electricity consumption for the purpose of short-term forecasting of electricity consumption.

Building an adaptive hybrid model for short-term prediction of power consumption using a neural network

This paper proposes a step-by-step technique for combining basic models that forecast electricity consumption in an artificial neural network by the method of preliminary selection and further hybridization. The reported experiments were conducted using data on hourly electricity consumption at the metallurgical plant AO ArcelorMittal Temirtau in the period from January 1, 2019, to November 30, 2021. The current research is related to the planned introduction of a balancing electricity market. 96 combinations of basic models were compiled, differing in the type of neural network, the set of initial data, the order of lag, the learning algorithm, and the number of neurons in the hidden layer. It has been determined that the NARX-type network is the most optimal architecture to forecast electricity consumption. Based on experimental studies, the number of hidden neurons needed to form a planned daily profile should equal 3 or 4; it is recommended to use the conjugate gradient method as a learning algorithm. When selecting models from three groups, it was revealed that the conjugate gradient method produces better results compared to the Levenberg-Marquardt algorithm. It is determined that the values of the selected RMSE error indicator take values of 23.17, 22.54, and 22.56, respectively, for the first, second, and third data groups. The adaptive hybridization method has been shown to reduce the RMSE error rate to 21.73. However, the weights of the best models with values of 0.327 for the first group of data, and 0.336 for the second and third ones, show that the individual use of a separate combination of models is also applicable. The devised forecasting electricity consumption model can be integrated into an automated electricity metering system

Identification of a supercritical fluid extraction process for modelling the energy consumption

Supercritical carbon dioxide extraction has been established as a promising and clean technology alternative to conventional separation techniques. Despite a high energy demand of extraction processes, their energy analysis has been scarcely considered. In this study, a supercritical carbon dioxide batch extraction process was modelled through system identification, forming a full simulator of its control loops affecting the energy consumption. The modelling was based on data acquired through systematic approach including experimental design and identification of dynamic process responses and energy consumption. Regression analysis and 12 identified models for subprocesses showed feasible performance during simulations with experimental data. The best local model for a subprocesses exhibited a Mean Absolute Percentage Error of 3% with independent test data. Regression model for steady-state electricity consumption showed a Mean Absolute Percentage Error of 7.6%, also suggesting the existence of nonlinearities between the response and other process variables. The identification approach reveals new information on energy consumption and dynamics of energy consumption of supercritical extraction in transient operating conditions. The models can be applied for further developments in real-time energy monitoring and optimization of supercritical extraction processes.

Energy communities and key features emerged from business models review

Climate emergency has changed the way of supplying energy, replacing fossil fuels in favour of a transition to renewable sources crucial for the system decarbonization. This change has led to a review of the electricity consumption and distribution model. Recently end-users connected to distribution grids have taken an increasing active role in the electrical power system. Such a situation highlights how the combination between distributed generation-active user, through the concept of energy self-consumption that turns in energy sharing for a ‘collective’ form, allowed by virtual self-consumption, seems to be a good solution to achieve both climate targets and solve technical issues related to renewables penetration. Aggregation of end-users which produce and self-consume their energy within the same geographical perimeter express a new way to use renewable energy represented by the energy communities. The paper identifies the key elements that effect a business model for energy sharing by residential end-users equipped with small-scale renewable generation plants. In addition the simulation analysis investigate the feasibility from the aggregator point of view as well as the individual end-users evaluating also a minimum size for a viable renewable energy community.

Two-stage robust facility location problem with drones

The past few years have witnessed the increasing adoption of drones in various industries such as logistics, agriculture, military, and telecommunications. This paper considers a short-term post-disaster unmanned aerial vehicle (UAV) humanitarian relief application where first-aid products need to be delivered to the customer demand points. The presented problem, two-stage robust facility location problem with drones (two-stage RFLPD), incorporates the demand uncertainty using demand scenarios. This problem aims to find a location–allocation-assignment plan that has minimal two-stage total cost in the worst-case scenario of all the possible demand outcomes. Three different models of the problem are proposed, two of which incorporate a realistic UAV electricity consumption model while the last one has greater operational flexibility. The column-and-constraint generation method and Benders decomposition are used to solve the two models, and a thorough comparison among the deterministic facility location problem with drones (FLPD) models and three proposed models are also presented. Numerical analysis results show that the proposed model has significantly less average cost in the simulation runs compared to the deterministic FLPD.

Stacking Deep learning and Machine learning models for short-term energy consumption forecasting

Accurate prediction of electricity consumption is essential for providing actionable insights to decision-makers for managing volume and potential trends in future energy consumption for efficient resource management. A single model might not be sufficient to solve the challenges that result from linear and non-linear problems that occur in electricity consumption prediction. Moreover, these models cannot be applied in practice because they are either not interpretable or poorly generalized. In this paper, a stacking ensemble model for short-term electricity consumption is proposed. We experimented with machine learning and deep models like Random Forests, Long Short Term Memory, Deep Neural Networks, and Evolutionary Trees as our base models. Based on the experimental observations, two different ensemble models are proposed, where the predictions of the base models are combined using Gradient Boosting and Extreme Gradient Boosting (XGB). The proposed ensemble models were tested on a standard dataset that contains around 500,000 electricity consumption values, measured at periodic intervals, over the span of 9 years. Experimental validation revealed that the proposed ensemble model built on XGB reduces the training time of the second layer of the ensemble by a factor of close to 10 compared to the state-of-the-art , and also is more accurate. An average reduction of approximately 39% was observed in the Root mean square error.