Short-term Load Demand Research Articles

Robust load feature extraction based secondary VMD novel short-term load demand forecasting framework

Load forecasting, as a crucial component of the electricity market, plays a significant role in ensuring the secure operation and rational planning of the power grid. However, as the power system becomes increasingly intricate, the demands on load forecasting techniques have escalated. Consequently, to mitigate the errors in short-term load forecasting (STLF) caused by uncertainty factors and to accommodate daily forecasting under abnormal electricity load conditions, this paper proposes a hybrid load forecasting model that combines an improved Secondary Variational Mode Decomposition (SVMD) algorithm with the Informer model. Employing electricity load data from the Panama context, the data is divided into four distinct experimental cases. The outcomes manifest that in contrast to the baseline model, the proposed approach engenders a minimal reduction of 15.08%, 12.95%, and 13.21% in Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE), respectively. Furthermore, supplementary experimental results demonstrate that the model exhibits strong robustness.

A short-term load demand forecasting: Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) optimization algorithm analysis

A short-term load demand forecasting: Levenberg–Marquardt (LM), Bayesian regularization (BR), and scaled conjugate gradient (SCG) optimization algorithm analysis

Predictive energy control for grid-connected industrial PV-battery systems using GEP-ANFIS

Rising energy costs in Uganda's industrial sector have significantly increased manufacturing expenses and led to the closure of potential enterprises, underscoring the urgent need for efficient energy management solutions. The study presents an optimization-based predictive energy control model that integrates Gene Expression Programming (GEP) and Adaptive Network-based Fuzzy Inference Systems (ANFIS) for grid-connected solar PV-battery systems. The hybrid GEP-ANFIS model leverages GEP's strength in complex pattern recognition and ANFIS's adaptive learning capability to manage nonlinear data, resulting in more accurate and reliable energy predictions. The model utilizes historical load patterns, grid data, and solar PV/battery inputs to forecast load demand and solar power generation, optimizing energy usage based on Time of Use (TOU) pricing. The approach addresses the limitations of existing energy management techniques by enhancing predictive accuracy and ensuring seamless integration with industrial applications. The present work demonstrates that the proposed GEP-ANFIS double diode model consistently outperforms its counterparts, achieving a mean absolute percentage error (MAPE) of 7.25 %, a mean absolute deviation (MAD) of 2.95 %, and a root mean square error (RMSE) of 8.42 %. Moreover, the model exhibits superior accuracy in predicting short-term load demand, with a MAPE of 2.24 %, a MAD of 0.13 %, and an RMSE of 0.18 %. Additionally, the model results in an average energy cost reduction of 6.7 % for hybrid grid-connected solar PV/battery configurations, highlighting its economic benefits. The study's key contributions include the creation of a novel hybrid predictive model, significant cost savings, improved load prediction accuracy, and the integration of renewable energy sources into industrial energy management. The model's inferences can effectively address the challenges of rising energy costs, providing a robust framework for sustainable and efficient energy management in industrial settings.

Short-Term Load Demand Forecasting Based on Weather and Influencing Factors Using Deep Neural Network Experts for Sustainable Development Goal 7

Short-Term Load Demand Forecasting Based on Weather and Influencing Factors Using Deep Neural Network Experts for Sustainable Development Goal 7

An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience

The advent of smart grid technologies has facilitated the integration of new and intermittent renewable forms of electricity generation in power systems. Advancements are driving transformations in the context of energy planning and operations in many countries around the world, particularly impacting short-term horizons. Therefore, one of the primary challenges in this environment is to accurately provide forecasting of the short-term load demand. This is a critical task for creating supply strategies, system reliability decisions, and price formation in electricity power markets. In this context, nonlinear models, such as Neural Networks and Support Vector Machines, have gained popularity over the years due to advancements in mathematical techniques as well as improved computational capacity. The academic literature highlights various approaches to improve the accuracy of these machine learning models, including data segmentation by similar patterns, input variable selection, forecasting from hierarchical data, and net load forecasts. In Brazil, the national independent system operator improved the operation planning in the short term through the DESSEM model, which uses short-term load forecast models for planning the day-ahead operation of the system. Consequently, this study provides a comprehensive review of various methods used for short-term load forecasting, with a particular focus on those based on machine learning strategies, and discusses the Brazilian Experience.

Day-ahead forecasting of residential electric power consumption for energy management using Long Short-Term Memory encoder–decoder model

Energy management in smart buildings and energy communities needs short-term load demand forecasting for optimization-based scheduling, dispatch, and real-time operation. However, producing accurate forecasting for individual residential households is more challenging compared to the forecasting of load demand at the distribution level, which is smoother and benefits from statistical compensation of errors. This paper presents a day-ahead forecasting technique for individual residential load demand that is based on the Long Short-Term Memory encoder–decoder architecture, which is extended to consider possibly differing sets of past and future exogenous variables. A novel focus is posed on the validation of the proposed approach considering that it is tailored for use by energy management systems. A publicly available dataset was used for validation, and the approach was compared with three other methods, resulting in a reduction of the Mean Absolute Scaled Error by up to 8%.

Machine Learning Technique for the Prediction of Short-term Load Demand: A Case Study

The purpose of this paper is to present a machine-learning approach for forecasting short-term load demand in Kano. Artificial Neural Network (ANN) and Support Vector Machine (SVM) are applied to develop the model. Three independent variables are selected as inputs, and one output is used to discover the level of relationship among the variables that are independent. This approach can ascertain a more precise prediction of the short-term load demand compared to expensive and rigorous experimental techniques. The correlation coefficient (R), coefficient of determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE) were used as indicators to evaluate the prediction accuracy of the selected algorithms. ANN gives a close accurate output as follows: R=0.97539, R2=0.951385, MSE=0.003674 and RMSE=0.060369.

A novel forward operator-based Bayesian recurrent neural network-based short-term net load demand forecasting considering demand-side renewable energy

Currently, traditional electricity consumers are now shifting to a new role of prosumers since more integration of renewable energy to demand side. Accurate short-term load demand forecasting is significant to safe, stable, and reliable operation of a renewable energy-dominated power system. In this paper, a short-term load forecasting model based on a bidirectional long short-term memory network (Bi-LSTM) using kernel transfer operator is proposed to achieve short-term load demand forecasting. To consider the influence of seasonality, holiday effects and weather on load demand forecasting, and simultaneously to improve the accuracy and performance of the forecasting model, this paper implements the dimensionality reduction of the input data by introducing an improved kernel transfer operator based on the Perron-Frobenius method. On this basis, a Bayesian Bi-LSTM for short-term load demand forecasting is formulated to obtain the probability prediction interval of load demand. To verify the validity of the proposed method, the actual historical load demand in a certain region in China was used for model training and verification. The forecasting results are compared with several conventional load demand methods using probability prediction technics. Simulation analysis shows that the proposed method can effectively reflect the short-term uncertainty of load demand, and is superior to the conventional methods in terms of forecasting accuracy and computational performance.

PRONÓSTICO DE DEMANDA DE ENERGÍA ELÉCTRICA USANDO PROCESOS GAUSSIANOS: UN ANÁLISIS COMPARATIVO

Abstract—Load demand forecasting is an essential component for planning power systems, and it is an invaluable tool to grid operators or customers. Many methods have been proposed to provide reliable estimates of electric load demand, but few methods can address the problem of predicting energy demand from a probabilistic point of view. One of them is the Gaussian processes (GP) that considering an adequate covariance function are suitable tools to carry out this load forecasting task. In this article, we show how to use Gaussian processes to predict elec- trical energy demand. Additionally, we thoroughly test various covariance functions and provide a new one. The performance of the proposed methodology was tested on two real data sets, showing that GPs are competitive alternatives for short-term load demand forecasting compared to other state-of-the-art methods

Distributed Economic Optimal Scheduling Scheme for Ship-Integrated Energy System Based on Load Prediction Algorithm

In order to enhance navigation safety and promote environmental protection, this paper takes the problem of energy management in a ship-integrated energy system into consideration. According to the characteristics of navigation, an intelligent ship energy management model, simultaneously considering the social and economic benefits, has been proposed. Meanwhile, this paper analyzes a distributed optimal scheduling problem which considers renewable generation devices and an energy storage system. Combined with an electricity-power system and thermal-power system, we propose an optimal scheduling scheme to accurately meet the actual load demand based on the pre-results analyzed by the ensemble learning short-term load forecasting algorithm. In addition, the related stability analysis is given. Further, a series of simulation results have been presented, which denote that the proposed load forecasting algorithm can accurately analyze the short-term load demand trend, and the proposed optimization algorithm can effectively coordinate economic and environmental protection.

A Very Short-Term Probabilistic Prediction Interval Forecaster for Reducing Load Uncertainty Level in Smart Grids

Very short-term load demand forecasters are essential for power systems’ decision makers in real-time dispatching. These tools allow traditional network operators to maintain power systems’ safety and stability and provide customers energy with high reliability. Although research has traditionally focused on developing point forecasters, these tools do not provide complete information because they do not estimate the deviation between actual and predicted values. Therefore, the aim of this paper is to develop a very short-term probabilistic prediction interval forecaster to reduce decision makers’ uncertainty by computing the predicted value’s upper and lower bounds. The proposed forecaster combines an artificial intelligence-based point forecaster with a probabilistic prediction interval algorithm. First, the point forecaster predicts energy demand in the next 15 min and then the prediction interval algorithm calculates the upper and lower bounds with the user’s chosen confidence level. To examine the reliability of proposed forecaster model and resulting interval sharpness, different error metrics, such as prediction interval coverage percentage and a skill score, are computed for 95, 90, and 85% confidence intervals. Results show that the prediction interval coverage percentage is higher than the confidence level in each analysis, which means that the proposed model is valid for practical applications.

Nghiên cứu so sánh các phương pháp dự báo phụ tải ngắn hạn trong lưới điện phân phối

Short-term load forecasting plays an important role in building operation strategies and ensuring reliability of any electric power system. Generally, short-term load forecasting methods can be classified into three main categories: statistical approaches, artificial intelligence based-approaches and hybrid approaches. Each method has its own advantages and shortcomings. Therefore, the primary objective of this paper is to investigate the effectiveness of ARIMA model (e.g., statistical method) and artificial neural network (e.g., artificial intelligence based-method) in short-term load forecasting of distribution network. Firstly, the short-term load demand of Quy Nhon distribution network and short-term load demand of Phu Cat distribution network are analyzed. Secondly, the ARIMA model is applied to predict the load demand of two distribution networks. Thirdly, the artificial neural network is utilized to estimate the load demand of these networks. Finally, the estimated results from two applied methods are conducted for comparative purposes.

A Novel Short-Term Load Forecasting Method by Combining the Deep Learning With Singular Spectrum Analysis

One of the major issues about the operation of power systems is the prediction of load demand. Moreover, load forecasting is of prime concern to system operators. Recently, the integration of power system elements, such as renewable energy sources, energy storages and electricity vehicle, brings more challenges, particularly when there are large fluctuations in forecasting cycle. This study concentrates on short-term load demand forecasting and proposes a hybrid method that combines Singular Spectrum Analysis (SSA) with deep-learning Neural Network (NN) techniques. In the beginning, the SSA technique is applied as an initial filter to remove noises. Next, a hybrid neural network, including Backpropagation Neural Network (BPNN) and Long Short-Term Memory (LSTM), is developed and trained. Then, the trained network is used as the core forecasting algorithm. Each SSA has different forms to combine with neural networks. The performance of the proposed forecasting algorithm is demonstrated using the power demand data recorded in Taiwan. Furthermore, this study compares the forecasting results by five models, including SSA, SSA-BPNN, ANN, SSA-LSTM, Wavelet Neural Network (WNN) and LSTM. The forecasting results reveal that the proposed forecasting model using Singular Spectrum Analysis provides the best performance on load forecasts.

Efficient Neurofuzzy Model to Very Short-Term Load Forecasting

Since adaptive neurofuzzy inference systems are universal approximators that can be used in prediction applications, this study aims to determine the best parameters and their best architectures for the purpose of performing very short-term load demand forecasting in power distribution substations. The system inputs are load demand time series, consisting of data measured at five-minute intervals over seven days. Several input configurations and different architectures were examined to make the forecasting a step forward. The results provided by the adaptive neurofuzzy inference system in relation to the approaches found in the literature are promising.

電力需要長周期変動成分の短時間先予測手法

It is indispensable to forecast accurately the very short-term load demand to avoid undesirable disturbances in power system operations which deteriorate economical generations. The authors have so far developed a short-term forecasting method by using Local Fuzzy Reconstruction Method, a variant of the methods based on the chaos theory. However, this approach is unable to give accurate forecasting results in case where load demand consecutively exceeds the historical maximum or is lower than the minimum because forecasting is performed by the historical data themselves. Also, in forecasting holidays in summer, forecasting result of weekdays might appear due to similar demand trend.This paper presents novel demand forecasting methods that are able to make accurate forecasts by resolving the above mentioned problems. First, the new method improves the accuracy by extrapolating forecasted transition from the current point. Secondly, to eliminate miss forecast which may be occurred on holidays in summer, historical data are labeled by the information of the day of the week to distinguish similarly behaved weekdays’ load patterns.The proposed methods are applied to 10, 30, and 60 minutes ahead demand forecasting, and the accuracy is improved 10% to 20% compared with the method previously proposed.

A study of two on-line prediction algorithms

This paper is concerned with the problem of developing an efficient and reliable algorithm for the online prediction of the short term load demand of an interconnected power system. It is proposed that a suitable load model be first set up using a spectral representation of the periodic component of the weekly load profile and that a minimal order Kalman predictor be determined for the residual load component. Results of case studies are presented in order to illustrate the advantages of such decomposition compared to a simpler load decomposition.

Modelling and forecasting short-term load demand: A multivariate approach

A multivariable time series model is proposed for short-term load demand forecasting. Unlike other approaches, the order of the model is determined without first finding the coefficients of the model. The Hankel matrix used for determining the order is also utilized for estimating the parameters of the model. This is then compared with order determination using the AIC criterion. Actual data provided by the Ontario Hydro for four loading buses is used for five-minute and hourly forecasts. The results show that the proposed approach is very attractive.

Load models for short-term load forecasting in multinode power systems

The problem of on-line short-term load demand forecasting at all the major loading nodes of an interconnected power system is studied. A multivariable state space model is proposed. The innovations representation is utilized, so that an ordinary recursive least-squares algorithm can be used to give unbiased estimates of the model parameters. State estimation is carried out by using adaptive estimation techniques. The parameter identification and state estimation algorithms are combined in a bootstrap manner. A scheme for detecting abnormal load behaviour is proposed. A weather sensitive load model is also presented for longer load time forecasts. Actual data provided by the Ontario Hydro for four loading buses are used for five-minute, ten-minute and hourly forecasts. The results show that the proposed approach is very attractive.

Short-Term Load Demand Modeling and Forecasting: A Review

Both the off-line and on-line methods for short-term electric load forecasting are reviewed. Since identifying an adequate model is the most important problem of any forecasting technique, the literature is classified according to the modeling approaches used for representing the load demand. The merits and drawbacks of each approach and how different authors have applied it to the problem under consideration are stressed. Also included is recent work where multivariable identification techniques are used to model the load demand of all the major loading nodes of a large power system. The advantages and the difficulties of applying such techniques are discussed. As a conclusion, directions for future research and future development of available algorithms, which can improve the state of the art, are suggested.

Modelling and Forecasting Short-term Load Demand: A Multivariate Approach

A multivariable time series model is proposed for short-term load demand forecasting. Unlike other approaches, the order of the model is determined without first finding the coefficients of the model. The Hankel matrix used for determining the order is also utilized for estimating the parameters of the model. This is then compared with order determination using the AIC criterion. Actual data provided by the Ontario Hydro for four loading buses is used for five-minute and hourly forecasts. The results show that the proposed approach is very attractive.