The modeling of the relationships between the power loads and the variables that influence the power loads especially in the abnormal days is the key point to improve the performance of short-term load forecasting systems. To integrate the advantages of several forecasting models for improving the forecasting accuracy, based on data mining and artificial neural network techniques, an ensemble d...

<p>Load forecasting is a significant element in the energy management system of power systems. Precise load aids electric utilities to conduct decisions unit commitment, reduction spinning reserve capacity, and schedule device maintenance plan. Furthermore, contributes reducing generation cost, it fundamental reliability On other hand, short-term substantial for economic running. The prec...

As short-term load forecasting is essential for the day-to-day operation planning of power systems, we built an ensemble learning model to perform such Thai data. The proposed uses voting regression (VR), producing forecasts with weighted averages from five individual models: three parametric multiple linear regressors and two non-parametric machine-learning models. are models gradient-descent ...

In power generation, a 24-hour load profile can vary significantly throughout the day. Therefore, generation must be adjusted to reduce money loss due excess generation. This paper presents short-term forecasting (STLF) system design using artificial neural network (ANN). As ANN come in many different configurations, this analyzes best configuration via trial-and-error method. To train ANN, his...