Short-term Electricity Demand Research Articles

A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data

Accurate electricity demand forecasting serves as a vital planning tool, enhancing the reliability of management decisions. Apart from that, achieving these aims, particularly in managing peak demand, faces challenges due to the industry’s volatility and the ongoing increase in residential energy use. Our research suggests that employing deep learning algorithms, such as recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent units (GRU), holds promise for the accurate forecasting of electrical energy demand in time series data. This paper presents the construction and testing of three deep learning models across three separate scenarios. Scenario 1 involves utilizing data from all-day demand. In Scenario 2, only weekday data are considered. Scenario 3 uses data from non-working days (Saturdays, Sundays, and holidays). The models underwent training and testing across a wide range of alternative hyperparameters to determine the optimal configuration. The proposed model’s validation involved utilizing a dataset comprising half-hourly electrical energy demand data spanning seven years from the Electricity Generating Authority of Thailand (EGAT). In terms of model performance, we determined that the RNN-GRU model performed better when the dataset was substantial, especially in scenarios 1 and 2. On the other hand, the RNN-LSTM model is excellent in Scenario 3. Specifically, the RNN-GRU model achieved an MAE (mean absolute error) of 214.79 MW and an MAPE (mean absolute percentage error) of 2.08% for Scenario 1, and an MAE of 181.63 MW and MAPE of 1.89% for Scenario 2. Conversely, the RNN-LSTM model obtained an MAE of 226.76 MW and an MAPE of 2.13% for Scenario 3. Furthermore, given the expanded dataset in Scenario 3, we can anticipate even higher precision in the results.

Msf, a forecasting library to predict short-term electricity demand based on multiple seasonal time series

Transmission system operators have a growing need for more accurate forecasting of electricity demand. Current electricity systems largely require demand forecasting so that the electricity market establishes electricity prices as well as the programming of production units. The companies that are part of the electrical system use exclusive software to obtain predictions, based on the use of time series and prediction tools, whether statistical or artificial intelligence. However, the most common form of prediction is based on hybrid models that use both technologies. In any case, it is software with a complicated structure, with a large number of associated variables and that requires a high computational load to make predictions. On many occasions, the resources to reach these predictions are not available to researchers. In this paper we present a MATLAB® toolbox created to predict electrical demand. The toolbox implements multiple seasonal time series forecasting methods such as new generalized multiple seasonal Holt–Winters exponential smoothing models and neural network models, among others. The models presented include novel discrete interval mobile seasonalities (DIMS) to improve forecasting on special days. The paper also describes the computational analysis conducted to apply the toolbox in various electrical systems in Europe, where the results obtained can be seen. The use of this library opens a new way of research for the use of models with discrete and complex seasonalities in other fields of application.

Construction of short-term electricity demand streaming forecasting model in demand-side dynamic response

In the context of demand-side dynamic response, the electricity supply-demand relationship undergoes constant changes, and short-term electricity load exhibits strong randomness and volatility, making load conditions challenging to predict. Hence, this paper proposes a short-term electricity demand streaming forecasting model that combines wavelet decomposition with Random Forest to enhance the accuracy of short-term electricity load forecasting. This model establishes a load feature system, utilizing a three-scale wavelet decomposition algorithm to break down the load sequence into several sub-sequences of different frequency bands. Subsequently, Random Forest load forecasting models are separately established for these sub-sequences. The final load prediction is obtained through reconstruction. This approach enables quasi-real-time short-term forecasting analysis of demand-side electricity demand.

Modelling and Distribution of Electricity Load Forecasting in Nigeria Power System (Olu-Ode Community)

To plan for energy generation to fulfill customer demand as the population grows, load forecasting is often used to anticipate and predict a region's power demand growth. A power load To sell, plan, and purchase energy for power systems, forecasting might be employed. From electrical energy production through distribution, it is highly helpful. Power system forecasting may be broadly categorized into three classes: An hour to a week is considered short-term electric load forecasting, a week (7 days) to a year is considered medium-term electric load forecasting, and a year and beyond is considered long-term electric load forecasting. In emerging nations where the energy demand is erratic due to fast economic expansion and a rise in the rate of rural-urban migration, accurate load forecasting may aid in creating a strategy. Various load forecasting techniques, including expert systems, fuzzy logic, regression techniques, and artificial neural networks (ANN), were researched. However, current methods may only sometimes provide more accuracy in predicting short-term stress. To address this issue, a novel strategy for anticipating short-term load is put forward in this study. Long short-term memory (LSTM) and convolutional neural networks are included in the created approach. The technique is used to anticipate the short-term electrical demand for the power system in Nigeria. Additionally, the usefulness of the proposed method is confirmed by comparing the forecasting errors of the suggested method with those of other existing methods like the long short-term memory network, the radial basis function network, and the extreme gradient boosting algorithm. It is discovered that the suggested technique produces better short-term load forecasting precision and accuracy.

Energy demand forecasting of remote areas using linear regression and inverse matrix analysis

Efficient energy demand forecasting is pivotal for addressing energy challenges in remote areas of Bangladesh, where reliable access to energy resources remains a concern. This study proposes an innovative approach that combines linear regression analysis (LRA) and inverse matrix calculation (IMC) to forecast energy demand accurately in these underserved regions. By leveraging historical energy consumption data and pertinent predictors, such as meteorological conditions, population dynamics, economic indicators, and seasonal patterns, the model provides reliable forecasts. The application of the proposed methodology is demonstrated through a case study focused on remote regions of Bangladesh. The results showcase the approach's effectiveness in capturing the intricate dynamics of energy demand and its potential to inform sustainable energy management strategies in these remote areas. This research contributes to the advancement of energy planning and resource allocation in regions facing energy scarcity, fostering a path towards improved energy efficiency and development. These techniques can be applied to estimate short-term electricity demand for any rural or isolated region worldwide.

Electricity demand error corrections with attention bi-directional neural networks

Reliable forecast of electricity demand is crucial to stability, supply, and management of electricity grids. Short-term hourly and sub-hourly demand forecasts are difficult due to the stochastic nature of electricity demand. To improve the electricity demand forecasts, artificial neural networks are combined with attention-based bi-directional neural network as well as Error Correction methods, and Bayesian hyperparameter optimizations. As part of the hybrid VMD-CABLSTM-ANN-EC model, the intrinsic mode functions, representing distinct predictive features, are separated from electricity demand data using variational mode decomposition algorithm. For predicting each intrinsic mode function and respective error time series, convolutional neural networks-bi-directional long short-term memory algorithms are applied. In a second stage, error time series are split, followed by a model to predict the error series’ intrinsic mode functions, and a final integration stage to predict the error. The results of the proposed VMD-CABLSTM-ANN-EC model are benchmarked against decomposition-based and standalone models at substations in Queensland, Australia. The proposed VMD-CABLSTM-ANN-EC model outperformed all benchmark models. By splitting the predictor and target variables, it will be shown that the variational model decomposition method improves error corrections by revealing key features of historical demand data. Using error correction method in short-term electricity demand modelling can provide greater confidence in the prediction of electricity demand. The models can be used by energy providers for market analysis and insight research, to manage power failure risk, and make financial decisions.

Selected aspects of generating short-term electricity demand forecasts, taking into account renewable energy sources

The risk of a decline in the quality of electricity demand forecasts in the short term increases due to the increase in the installed capacity of renewable energy sources (RES). This is mainly due to the high daily variability of electricity production from renewable sources, which is strongly dependent on local weather conditions. Production from renewable energy sources is a very complex time series, additionally reinforced by a significant increase in its share in total production. This applies in particular to photovoltaic sources in low-voltage networks. There is therefore an urgent need to improve the quality of forecasts in this area. The main goal of the research was to verify statistical models that often achieve good results in the complex problem of forecasting electricity demand. The main objective, regarding daily forecasts of consumers' demand for electricity, was achieved through the implementation of intermediate objectives, including the development of a methodology for estimating electricity generated by photovoltaic installations.

Forecasting Short-Term Peak Load Demand in the Libyan Power Grid using Multiple Regression Model

The demand for energy, particularly electricity, has been rising rapidly around the world and plays an important role in socio-economic development, especially in developing countries. Electricity load forecasting is a key task in power plant planning, as well as in efficient operation and sustainable growth of modern electricity distribution networks. This is to make Balancing the relationship between electricity demand and supply. The main purpose of this study is to investigate the impact of meteorological factors such as temperature, relative humidity and wind speed on short-term electricity demand forecasts in Libya. A model has been developed by means of multiple regression analysis and trend method techniques. It was found that there is a good correlation between the factors and electricity use. Since the final model exhibits some little deviation, this approach has proven to be helpful for predicting Libya's electricity consumption. The findings are analyzed and discussed in order to offer recommendations for using this approach for forecasting the short-term demand of electricity.

Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting.

Seemingly unrelated time series model for forecasting the peak and short-term electricity demand: Evidence from the Kalman filtered Monte Carlo method

In this extant paper, a multivariate time series model using the seemingly unrelated times series equation (SUTSE) framework is proposed to forecast the peak and short-term electricity demand using time series data from February 2, 2014, to August 2, 2018. Further the Markov Chain Monte Carlo (MCMC) method, Gibbs Sampler, together with the Kalman Filter were applied to the SUTSE model to simulate the variances to predict the next day's peak and electricity demand. Relying on the study results, the running ergodic mean showed the convergence of the MCMC process. Before forecasting the peak and short-term electricity demand, a week's prediction from the 28th to the 2nd of August of 2018 was analyzed and it found that there is a possible decrease in the daily energy over time. Further, the forecast for the next day (August 3, 2018) was about 2187 MW and 44090 MWh for the peak and electricity demands respectively. Finally, the robustness of the SUTSE model was assessed in comparison to the SUTSE model without MCMC. Evidently, SUTSE with the MCMC method had recorded an accuracy of about 96% and 95.8% for Peak demand and daily energy respectively.

Short-Term EV Charging Demand Forecast with Feedforward Artificial Neural Network

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.

Short-term electricity demand forecasting using a hybrid ANFIS–ELM network optimised by an improved parasitism–predation algorithm

One of the most crucial steps in comprehensively planning the proper and efficient use of energy (optimisation of consumption) and appropriate management of resources to fulfil the power demand is accurately forecasting the energy demand (optimisation of production). This study presents the modelling of an ideal technique for short-term power demand prediction as a novel hybrid approach comprising two distinct methods, namely the Elman neural network (ELM) and adaptive network–basedfuzzy inference system (ANFIS). The hybrid approach outperforms the conventional individual methods because it can eliminate the drawbacks of the individual methods while retaining their advantages, particularly considering that ELM and ANFIS can handle non-linear data. The weight coefficients of the procedures in the proposed hybrid approach were determined using a novel, enhanced bioinspired algorithm, called the improved parasitism–predation algorithm, to achieve better accuracy. The simulation results demonstrated the superiority of the proposed approach over other state-of-the-art approaches including the independent ELM and ANFIS.

Electricity Demand Forecasting using LSTMs

Electricity demand forecasting is an essential task in the energy industry, enabling utilities and energy suppliers to optimize the generation, transmission, and distribution of electricity. In recent years, deep learning techniques such as Long Short -Term Memory (LSTM) neural networks have shown great potential in improving the accuracy and efficiency of time-series forecasting tasks, including electricity demand forecasting. This research proposes an LSTM-based neural network architecture for short-term electricity demand forecasting. The proposed model is evaluated on real-world electricity demand data, and the results demonstrate its effectiveness in predicting future demand patterns. The model's performance is evaluated using the Mean Squared Error loss function and the Root Mean Squared Error metric. The proposed model shows promising results compared to traditional time-series forecasting models. The results suggest that LSTM-based neural networks can be a powerful tool for electricity demand forecasting, providing more accurate and efficient forecasting models that can help improve energy system planning and decision making.

Probabilistic Forecasting of Electricity Demand Incorporating Mobility Data

Due to extreme weather conditions and anomalous events such as the COVID-19 pandemic, utilities and grid operators worldwide face unprecedented challenges. These unanticipated changes in trends introduce new uncertainties in conventional short-term electricity demand forecasting (EDF) since its result depends on recent usage as an input variable. In order to quantify the uncertainty of EDF effectively, this paper proposes a comprehensive probabilistic EFD method based on Gaussian process regression (GPR) and kernel density estimation (KDE). GPR is a non-parametric method based on Bayesian theory, which can handle the uncertainties in EDF using limited data. Mobility data is incorporated to manage uncertainty and pattern changes and increase forecasting model scalability. This study first performs a correlation study for feature selection that comprises weather, renewable and non-renewable energy, and mobility data. Then, different kernel functions of GPR are compared, and the optimal function is recommended for real applications. Finally, real data are used to validate the effectiveness of the proposed model and are elaborated with three scenarios. Comparison results with other conventional adopted methods show that the proposed method can achieve high forecasting accuracy with a minimum quantity of data while addressing forecasting uncertainty, thus improving decision-making.

Short-term electric vehicle battery swapping demand prediction: Deep learning methods

Battery swap stations have become an important alternative to general charging posts. Predicting battery swapping demand at the station level would be helpful for real-time operation of stations. This paper first provided insights into battery swapping demand patterns by analyzing a real-world dataset which contained 2,529 battery swapping events collected from 36 battery swap stations in Beijing from 31st July to 20th August 2019. Further, we developed a series of deep learning methods to predict the EV battery swapping demand, particularly considering temporal demand patterns obtained from the dataset. The deep learning models were Long Short-Term Memory, Bidirectional Long Short-Term Memory, Gated Recurrent Units, and Bidirectional Gated Recurrent Units. The results showed that the four deep learning models outperformed typical machine learning methods (e.g., support vector regression). An ablation study indicated that incorporating temporal battery swapping demand patterns into the deep learning methods could greatly improve model performance.

Short-term electric vehicle charging demand prediction: A deep learning approach

Short-term prediction of the Electric Vehicle (EV) charging demand is of great importance to the operation of EV fleets and charging stations. This paper develops a Long Short-Term Memory (LSTM) neural network to predict the EV charging demand at the station level for the next few hours (e.g., 1–5 h), using a unique trajectory dataset containing over 76,000 private EVs in Beijing in January 2018. To explore the performance of the LSTM model, we set up four scenarios by 1) comparing LSTM against two typical time series prediction models, i.e., the Auto-Regressive Moving Average model (ARIMA), and the Multiple Layer Perceptron model (MLP), 2) and investigating how different input data structures, sample sizes, and time spans and intervals would influence model accuracy. The results suggest that the LSTM model outperformed the ARIMA, and MLP models, and their MAPE1 values are 6.83 %, 21.58 %, and 18.31 %, respectively. In addition, we find that the time span and interval tend to be more influential to the LSTM model’s prediction accuracy than input data structures, and sample sizes. In general, the LSTM model with a shorter time span or interval (e.g., 1 h) would perform better.

Research On The Daily Electricity Forecast Model Based On LightGBM

Short-term electricity demand forecast is of great significance for guiding the Power Grid Company production operation, but the existing forecast mainly concentrated on load forecast, and most of them only use meteorological and date factors as input features, ignoring the economic factors as a basis to support the growth of electricity, also doesn’t take into account the influence of recent significant events. To improve the accuracy and rationality of short-term electricity demand forecast, based on the Light Gradient Boosting Machine (LightGBM) model, in addition to the common meteorological and date factors, all kinds of economic and significant events are introduced into the model to forecast the daily electricity in this paper. The results show that economic and significant events provide useful information, making our model more accurate than previous short-term electricity demand forecasts. Moreover, due to the adoption of machine learning algorithms, our model is more stable and accurate than traditional measurement models with the same factors.

Predicting the short-term electricity demand based on the weather variables using a hybrid CatBoost-PPSO model

In this study, by using the capabilities of the CatBoost model and meta-heuristic algorithms, as well as the hybridization technique, an attempt was made to improve the prediction of electricity demand on a short-term scale. For this purpose, the hybrid CatBoost-PPSO model was suggested in this study to predict electricity demand based on weather variables. Finally, by conducting a case study and comparing the results of the proposed model with five other hybrid models, the results were evaluated and compared using various statistical indexes. The general approach used in this study is that the hyper-parameters of the Catboost were optimized using a meta-heuristic algorithm, and the best of them were used during the forecasting process. Also, during the network training, the K-Fold cross-validation algorithm is used to avoid over-fitting. The evaluation results of the models based on the test data showed that the hybrid CatBoost-PPSO model has high capabilities in short-term electricity demand forecasting. The indices obtained from this model show better values than other hybrid models. For example, the RMSE value of this model is equal to 42.3, which shows an improvement of almost 9.5% compared to the hybrid CatBoost-ALO model.

Assessment of hydroelectric potential under climate change and hydrological parameters based on soft-computing: A case study

Electricity is recognized as one of the most pervasive government services. Therefore, accurate forecasting of electricity demand has a great impact on electricity management. In this paper, the monthly electricity demand has been forecasted. For this objective, an integrated Artificial Neural Network (ANN) with a developed metaheuristic algorithm has been used to forecast short-term electricity demand. The improved algorithm reduces errors in the optimization process by preventing a fall into local optimization. This algorithm is called the Developed African Vulture Optimization (DAVOA) algorithm. The finding indicates the annual contribution of temperature, relative moisture, wind speed, and cloudiness to forecast short-term electricity demand by about 70%, 12%, 10%, and 8%, respectively. Therefore, temperature, with a correlation of 0.70%, is an important variable that affects short-term energy demand. Moreover, the finding of short-term energy demand based on the optimal ANN method indicates that the electricity demand change in January, February, July, August, and September is greater than in other months because the temperature changes more drastically in these months. Therefore, the use of heating and cooling tools will increase compared to other months, and consequently, the energy demand for the energy supply of cooling and heating tools will increase.

An exposition on the prediction of load on a Smart Grid

Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.