In the presence of deregulated electric industry, load forecasting is more demanded than ever to ensure execution applications such as energy generation, pricing decisions, resource procurement, and infrastructure development. This paper presents a hybrid machine learning model for short-term (STLF) by applying random forest bidirectional long memory acquire benefits both methods. experimental ...

This paper presents the application of Fuzzy ARTMAP neural network for evaluating on-line load forecasting in short term case. A new approach using artificial neural networks (ANNs) is proposed for short term load forecasting. To forecast loads of a day, the hourly load pattern and the maximum and minimum and average of temprature must be determined. To demonstrate the effectiveness of the prop...

Load Forecasting plays a key role in making today's and future's Smart Energy Grids sustainable and reliable. Accurate power consumption prediction allows utilities to organize in advance their resources or to execute Demand Response strategies more effectively, which enables several features such as higher sustainability, better quality of service, and affordable electricity tariffs. It is eas...

Load forecasts are fundamental inputs for the reliable and resilient operation of a power system. Globally, researchers endeavor to improve resulting forecast accuracies. However, lack studies detailing standardized model development process remains major issue. In this regard, paper advances knowledge systematic short-term load (STLF) electric utilities. The proposed has been developed by usin...

Aim of this research is to develop a hybrid prediction model based on Artificial Neural Network (ANN) and Genetic Algorithm (GA) that integrates the benefits of both techniques to increase the electrical load forecast accuracy. Precise Short Term Load Forecast (STLF) is of critical importance for the secure and reliable operation of power systems. ANNs are largely implemented in this domain due...

Abstract Sustainable energy systems are characterised by an increased integration of renewable sources, which magnifies the fluctuations in supply. Methods to cope with these magnified fluctuations, such as load shifting, typically require accurate short-term forecasts. Although numerous machine learning models have been developed improve forecasting (STLF), often large amounts training data. U...

A precise short-term load forecasting technique is required for the economic and reliable operation of power system. Modern load forecasting techniques especially ANN methods are attractive as they have the ability to handle the non-linear relationships between load, weather temperature and the factors affecting it directly. In this paper, an investigation on the use of ANN for short term load ...