Short-term Electricity Consumption Research Articles

An Improved MGM (1, n) Model for Predicting Urban Electricity Consumption

The MGM (1, n) model has the characteristics of less data required, simple modeling, and high prediction accuracy. It has been successfully applied to short-term forecasting across various economic, social, and technological domains, yielding promising outcomes. There is insufficient attention paid to the interpolation coefficient of the model. The interpolation coefficients determine the extent of model fitting, which, in turn, impacts its prediction accuracy. This study made some improvements to the interpolation coefficients and proposed an improved MGM (1, n) model. IMGM (1, n) model and MGM (1, n) model were employed to compare the performance of the improved MGM (1, n) model. Upon a series of comparisons and analyses, it was concluded that the improved MGM (1, n) model has higher fitting and prediction accuracy than the other two forecasting methods. The method was used to forecast the short-term electricity consumption of Linfen City. The findings revealed that by 2030, the electricity demand in Linfen City is projected to be 563.7 billion kWh.

Short-Term Electricity Consumption Forecasting at Residential Level Using Two-Phase Hybrid Machine Learning Model

Short-Term Electricity Consumption Forecasting at Residential Level Using Two-Phase Hybrid Machine Learning Model

A novel dispatch adaptation load feature mapping network for multi-timestep load forecast

Power system dispatch (PSD) greatly depends on load forecast (LF) with high accuracy. However, since load curve evolving along time axis is affected by long-term trendy and short-term stochastics electricity consumption modes, it is not easy to forecast load under accuracy requirement of PSD especially for high-proportion renewable energy power system. Therefore, a novel dispatch adaptation load feature mapping network with coordinated memory (LFMN-CN), which can forecast multi-timestep load values in future dispatch span at a time, is proposed. It adopts layered mapping network structure: 1) load features based on periodicity are mapped into two-dimension input matrix in the first layer; 2) nodes in the hidden layer with long-term and current memory, which denote long-term trendy and short-term stochastics electricity consumption modes respectively, are fully connected to construct recurrent LF network; 3) multi-timestep LF values in outer layer are obtained by LF output vector adaptive to timesteps of dispatch span. It has advantages of improved accuracy and dependence only on historical load series. LF results in power system of China show that the proposed model can perform multi-timestep LF more accurately than single-timestep LF.

COMPARISON OF SHORT-TERM FORECASTING METHODS OF ELECTRICITY CONSUMPTION IN MICROGRIDS

Context. The current stage of development of the electric power industry is characterized by an intensive process of microgrid development and management. The feasibility of using a microgrid is determined by the fact that it has a number of advantages compared to classical methods of energy generation, transmission, and distribution. It is much easier to ensure the reliability of electricity supply within the microgrid than in large energy systems. Energy consumers in a microgrid can affect the power balancing process by regulating their loads, generating, storing, and releasing electricity. One of the main tasks of the microgrid is to provide consumers with electrical energy in a balance between its generation and consumption. This is achieved thanks to the intelligent management of the microgrid operation, which uses energy consumption forecasting data. This allows to increase the efficiency of energy infrastructure management.
 Objective. The purpose of this work is to develop short-term electricity consumption forecasting models for various types of microgrid electricity consumers, which will improve the efficiency of energy infrastructure management and reduce electricity consumption.
 Method. The SARIMA autoregressive model and the LSTM machine learning model are used to obtain forecast values of electricity consumption. AIC and BIC information criteria are used to compare autoregressive models. The accuracy of forecasting models is evaluated using MAE, RMSE, MAPE errors.
 Results. The experiments that forecast the amount of electricity consumption for the different types of consumers were conducted. Forecasting was carried out for both LSTM and AR models on formed data sets at intervals of 6 hours, 1 day, and 3 days. The forecasting results of the LSTM model met the forecasting requirements, providing better forecasting quality compared to AR models.
 Conclusions. The conducted study of electricity consumption forecasting made it possible to find universal forecasting models that meet the requirements of forecasting quality. A comparative analysis of developed time series forecasting models was performed, as a result of which the advantages of ML models over AR models were revealed. The predictive quality of the LSTM model showed the accuracy of the MAPE of forecasting electricity consumption for a private house – 0.1%, a dairy plant – 3.74%, and a gas station – 3.67%. The obtained results will allow to increase the efficiency of microgrid management, the distribution of electricity between electricity consumers to reduce the amount of energy consumption and prevent peak loads on the power grid.

Optimized multivariate grey forecasting model for predicting electricity consumption: A China study

The power industry has significantly contributed to the prosperity of the national economy, and accurate prediction can reflect the development trend of the power system and power market. The short-term electricity consumption of a country exhibits both annual growth certainty and random change uncertainty, which can be suitably considered with the grey forecasting model. Regarding the short-term trends of electricity consumption in China, this study established an optimized multivariate grey forecasting model with variable background values (OGM(1, N) model) to forecast the electricity consumption level in China. The established model could be converted into the GM(1, N) model and different variant models by adjusting the model parameters. With Beijing, Tianjin and Shanghai as examples, the OGM(1, N) model is compared to the GM(1, N) model and its variant model. The excellent prediction results confirm the feasibility of the proposed model. Then, the proposed model is applied to study China’s electricity consumption. The research results indicated that the OGM(1, N) model attains an extraordinarily high precision in the prediction of electricity consumption and can provide a practical reference for accurate electricity consumption prediction.

Smart Meter Data Obfuscation With a Hybrid Privacy-Preserving Data Publishing Scheme Without a Trusted Third Party

Smart electricity meters as a prominent instance of the Internet of Things (IoT) have driven more efficient energy services in smart grids but also created growing concerns of consumer privacy. Homomorphic encryption of consumption data is a conventional solution for privacy protection, but it incurs a high computational burden to the resource-restrained smart meters (SMs) due to the encryption of high-frequency consumption readings. Perturbation is another major approach in providing the privacy protection of SM readings which is highly efficient. However, most existing perturbation-based works inadequately balance the tradeoff between the need of hiding individual consumption profiles and the need of retaining utility’s quality. This article proposes a hybrid privacy-preserving electricity consumption data publishing scheme without a trusted third party which utilizes the desirable properties of both perturbation and cryptography for better privacy-utility tradeoff and efficiency. The proposed scheme for fine-grained SM consumption data consists of two phases, which are noise generation and noise distribution. In the first phase, a distributed perturbation method is designed to provide differential privacy protection for high-frequency consumption while retaining the accuracy of energy services like regional load forecasting. In the second phase, a private noise distribution protocol, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$nn$ </tex-math></inline-formula> -PND, securely distributes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> noise elements generated by an energy distribution operator to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> SMs in a semihonest adversarial model. Formal proofs of correctness and privacy of the scheme are provided. Experiments of regional short-term electricity consumption forecast using real-world data sets demonstrate the preservation of the utility over the masked data of this scheme.

Short-term electricity consumption forecasting with NARX, LSTM, and SVR for a single building: small data set approach

ABSTRACT Nowadays, there is an undoubted change of trend toward a decentralized and decarbonized electric grid, where the electric generation based on local resources will take on special relevance. In this context, the encouragement of collective self-consumption (CSC) becomes one of the key issues. One of the aspects that will contribute to this aim is the development of power consumption-forecasting tools. This article proposes the comparison of three models to perform a day ahead consumption forecasting of ESTIA 2 building: nonlinear autoregressive neural network with exogenous inputs (NARX), long-short-term memory cell (LSTM) and support vector regression (SVR). First, the model structure has been designed by selecting the suitable-input combination and the optimal time window (TW) for the three models. Then, parameters of each model have been adjusted to achieve the most accurate prediction. After forecasting separately winter and summer seasons, experiments reveal that the proposed NARX neural network is the one that predicts with the highest accuracy in both winter and summer months, obtaining a mean absolute percentage error (MAPE) of 14,1% and 12%, respectively. Likewise, regardless of the model, better results have been obtained in summer predictions, which is closely related to the dependence of the building’s consumption on the heating, ventilation, and air conditioning (HVAC) system.

Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic

Accurate load forecasting is essential for power-sector planning and management. This applies during normal situations as well as phase changes such as the Coronavirus (COVID-19) pandemic due to variations in electricity consumption that made it difficult for system operators to forecast load accurately. So far, few studies have used traffic data to improve load prediction accuracy. This paper aims to investigate the influence of traffic data in combination with other commonly used features (historical load, weather, and time) – to better predict short-term residential electricity consumption. Based on data from two selected distribution grid areas in Switzerland and random forest as a forecasting technique, the findings suggest that the impact of traffic data on load forecasts is much smaller than the impact of time variables. However, traffic data could improve load forecasting where information on historical load is not available. Another benefit of using traffic data is that it might explain the phenomenon of interest better than historical electricity demand. Some of our findings vary greatly between the two datasets, indicating the importance of studies based on larger numbers of datasets, features, and forecasting approaches.

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.

SmartEle: Smart Electricity Dashboard for Detecting Consumption Patterns: A Case Study at a University Campus

To achieve Sustainable Development Goal 7 (SDG7), it is essential to detect the spatiotemporal patterns of electricity consumption, particularly the spatiotemporal heterogeneity of consumers. This is also crucial for rational energy planning and management. However, studies investigating heterogeneous users are lacking. Moreover, existing works focuses on mathematic models to identify and predict electricity consumption. Additionally, owing to the complex non-linear interrelationships, interactive visualizations are more effective in detecting patterns. Therefore, by combining geospatial dashboard knowledge and interactive visualization technology, a Smart Electricity dashboard (SmartEle) was designed and developed to interactively visualize big electrical data and interrelated factors. A university campus as the study area. The SmartEle system addressed three challenges. First, it permitted user group-oriented monitoring of electricity consumption patterns, which has seldom been considered in existing studies. Second, a visualization-driven data mining model was proposed, and an interactive visualization dashboard was designed to facilitate the perception of electricity usage patterns at different granularities and from different perspectives. Finally, to deal with the non-linear features of electricity consumption, the ATT-LSTM machine learning model to support multivariate collaborative predicting was proposed to improve the accuracy of short-term electricity consumption predictions. The results demonstrated that the SmartEle system is usable for electricity planning and management.

A short‐term electricity consumption forecasting approach based on feature processing and hybrid modelling

The short-term electricity consumption forecasting can help to ensure the safe and reliable operation of the power system. Power companies usually need to report the electricity consumption of the current month five to seven days in advance and make a power generation plan for the next month. The existing studies are usually lack of appropriate feature selection methods and hard to achieve satisfactory results. This paper proposes a short-term electricity consumption forecasting approach based on feature processing and hybrid modelling. The maximum information coefficient (MIC) is employed to analyse the feature correlation, the electricity consumption curves are converted to several sub-sequences of different frequency bands by the variational mode decomposition (VMD) to describe signal characteristics accurately, a hybrid model based on bidirectional gated recurrent unit (BiGRU) is innovated to extract the temporal and spatial features of the data and capture the contextual information from the complete time series, attention mechanism is used to do extract useful information and assign weights to make forecast. Compared with several benchmark methods, the proposed approach achieves better electricity consumption curve fitting and higher forecasting accuracy with the increase of forecasting step size.

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.

Short-term electricity consumption forecasting method based on empirical mode decomposition of long-short term memory network

Accurate electricity consumption forecasting can improve the efficiency of grid dispatching and effectively guarantee the stable operation of the power system. Electricity consumption forecasting is important for the analysis of customer-side electricity consumption behavior, but the instability of electricity consumption sequences poses difficulties for forecasting. Therefore, an improved combination of integrated empirical modal decomposition (EMD) and long short-term memory network (LSTM) is proposed for customer-side electricity consumption forecasting. This paper starts from the idea of blind source separation and independent prediction, and firstly decomposes the original electricity consumption data into several inherent mode functions (IMFs) with different frequencies and amplitudes by empirical modal decomposition (EMD), and then uses LSTM to extract features and make temporal prediction for each IMF component one by one with machine learning intelligent algorithm., and finally obtains end-user-side short-term electricity consumption prediction results by accumulating multiple target prediction results. Compared with direct forecasting, the proposed EMD-LSTM independent forecasting model is able to identify the characteristics of each frequency component of electricity consumption data, and its error is reduced by about 15% on average, thus achieving the goal of improving the accuracy of load forecasting in short-term electricity consumption forecasting scenarios.

A New CNN-Based Method for Short-Term Forecasting of Electrical Energy Consumption in the Covid-19 Period: The Case of Turkey

This study proposes a new convolutional neural network (CNN) method with an input-signal decomposition algorithm. With the proposed CNN architecture, hourly electricity consumption data for the Covid-19 period in Turkey were used as input data, and the short-term electricity consumption was forecasted. The input data were decomposed into its subcomponents using a signal decomposition process called Empirical Mode Decomposition (EMD). To extract the deep features, all input data were transformed into 2D feature maps and fed into the CNN. The obtained results were compared with the pre-trained models GoogleNet, AlexNet, SqueezeNet, and ResNet18. Model-wise comparisons showed that the proposed method had the highest correlation coefficient (R) and lowest root mean square error (RMSE) and mean absolute error (MAE) values for 1-h, 2-h, and 3-h. The mean R-values of the proposed method were 95.6&#x0025;, 95.2&#x0025;, and 94.0&#x0025; for 1h, 2h and 3h ahead, respectively. The mean RMSE values were 8.2&#x0025;, 8.7&#x0025;, and 10.2&#x0025; for 1h, 2h and 3h ahead, respectively. The experimental results confirm that the proposed method outperforms other pretrained methods despite its simpler structure.

A Neural Network Mid-Term and Short-Term Electricity Consumption Forecast Model Based on Multiple Influencing Factors

A Neural Network Mid-Term and Short-Term Electricity Consumption Forecast Model Based on Multiple Influencing Factors

A combined deep learning load forecasting model of single household resident user considering multi-time scale electricity consumption behavior

With the growth of residential load and the popularity of intelligent devices, resident users have become important target customers for demand response (DR). However, due to the strong volatility of individual household load and the large difference in user’s behavior, the accuracy of residential load forecasting is generally low and the forecasting effect is unstable, which is not conductive to the implementation of DR. To improve the accuracy of residential load forecasting, this paper proposes a combined deep learning load forecasting model considering multi-time scale electricity consumption behavior of single household resident user to achieve high-accuracy and stable load forecasting. Aiming at the electricity consumption behavior, the multi-time scale similarity analysis is carried out. For the time scale of one year, Normalized Dynamic Time Warping (N-DTW) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) are used to analyze the significance of single user's long-term electricity consumption behavior. For the time scale of 7 days, behavior similarity is used to analyze the consistency of single user's short-term electricity consumption behavior. Then, Mutual Information (MI) and Principal Component Analysis (PCA) are used to select features and reduce dimensions of multi-dimensional weather influencing factors, so as to avoid the interference of irrelevant factors and improve the calculation speed. On this basis, combined with Back Propagation (BP) neural network, Extreme Gradient Boosting (XGBoost) and Long Short-Term Memory (LSTM) neural network, a combined deep learning network load forecasting model (Co-LSTM) is constructed by using multi-model and multi-variable method to achieve stable and high-accuracy load forecasting. Finally, based on the actual load data from the American Pecan Street Energy Project, the forecasting accuracy of the proposed model of resident user is evaluated. From the performance of load forecasting for 42 target users, the minimum, maximum and average Mean Arctangent Absolute Percentage Error (MAAPE) of Co-LSTM is 18.70%, 45.95% and 31.20% (the average MAAPE is 4.97% less than the traditional LSTM model) respectively.

Performance Evaluation of Forecasting Strategies for Electricity Consumption in Buildings

In this paper, three main approaches (univariate, multivariate and multistep) for electricity consumption forecasting have been investigated. In fact, three major algorithms (XGBOOST, LSTM and SARIMA) have been evaluated in each approach with the main aim to figure out which one performs the best in forecasting electricity consumption. The motivation behind this work is to assess the forecasting accuracy and the computational time/complexity for an embedded forecasting and model training at the smart meter level. Moreover, we investigate the deployment of the most efficient model in our platform for an online electricity consumption forecasting. This solution will serve for deploying predictive control solutions for efficient energy management in buildings. As a proof of concept, an already existing public dataset has been used. These data were mainly collected thanks to the usage of already deployed sensors. These provide accurate data related to occupancy (e.g., presence) as well as contextual data (e.g., disaggregated electricity consumption of equipment). Experiments have been conducted and the results showed the effectiveness of these algorithms, used in each approach, for short-term electricity consumption forecasting. This has been proved by performance evaluation and error calculations. The obtained results mainly shed light on the challenging trade-off between embedded forecasting model training and processing for being deployed in smart meters for electricity consumption forecasting.

One common enemy, a pandemic, uniting interdisciplinary teams

One common enemy, a pandemic, uniting interdisciplinary teams

Forecasting short-term electricity consumption in Burkina Faso

In this study, we us the standard Box and Jenkins methodology to build a forecasting model for short-term electricity consumption in Burkina Faso. Using data from 1999 to 2019, our model predicts a 6.9% increase in electricity consumption in 2020 compared to 2019. In addition, the series will follow the same trend along with the seasonal variation.

Short-Term Electricity Consumption Forecasting Based on the EMD-Fbprophet-LSTM Method

Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.