Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths weaknesses. This paper comprehensively reviews some models, including time series, artificial neural networks (ANNs), regressi...

There is a lot of research on the neural models used for short-term load forecasting (STLF), which crucial improving sustainable operation energy systems with increasing technical, economic, and environmental requirements. Neural networks are computationally powerful; however, lack clear, readable trustworthy justification STLF obtained using such serious problem that needs to be tackled. The a...

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity c...

This paper presents short term load forecasting (STLF) in Java Island using recurrent neural network (RNN). The simple one of RNN is Elman, it has one hidden layer and suitable used in time series prediction. It can learn an input-output mapping which is nonlinear. The Elman RNN was proposed for one day a head forecasting, with interval time 30 minutes. Training model divided into weekday, week...

This paper presents a novel hybrid method for short-term load forecasting. The system comprises of two artificial neural networks (ANN), assembled in a hierarchical order. The first ANN is a multilayer perceptron (MLP) which functions as integrated load predictor (ILP) for the forecasting day. The output of the ILP is then fed to another, more complex MLP, which acts as an hourly load predictor...

A hybrid feature selection (HFS) algorithm to obtain the optimal set attain forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic (EGA) and random forest method, which embedded online (FS). Using selected features, performance of forecaster was tested signify utility methodology. For this, a day-ahead STLF using M5P (a co...

Forecasting electricity load demand is critical for power system planning and energy management. In particular, accurate short-term forecasting (STLF), which focuses on the lead time horizon of few minutes to one week ahead, can help in better scheduling, unit commitment, cost-effective operation smart grids. last decade, different artificial intelligence (AI)-based techniques metaheuristic alg...

The short-term load forecasting (STLF), with lead times ranging from a few hours to several days ahead, helps grid operators to make a cost effective scheduling of resources, purchase of energy, maintenance and security analysis studies. The use of reliable load forecasting models is necessary for a rational use of electricity, taking into account that it is not storable. Climatic conditions ce...

abstract forecasting electrical energy demand and consumption is one of the important decision-making tools in distributing companies for making contracts scheduling and purchasing electrical energy. this paper studies load consumption modeling in hamedan city province distribution network by applying esn neural network. weather forecasting data such as minimum day temperature, average day temp...

A reliable and accurate short-term load forecasting (STLF) helps utilities energy providers deal with the challenges posed by supply demand balance, higher penetration of renewable energies development electricity markets increasingly complex pricing strategies in future smart grids. Recent advances deep learning have been successively utilized to STLF. However, there is no certain study that e...