An effective forecasting model for short-term load plays a significant role in promoting the management efficiency of an electric power system. This paper proposes a new forecasting model based on the improved neural networks with random weights (INNRW). The key is to introduce a weighting technique to the inputs of the model and use a novel neural network to forecast the daily maximum load. Ei...

In recent years, Secondary Substations (SSs) are being provided with equipment that allows their full management. This is particularly useful not only for monitoring and planning purposes but also for detecting erroneous measurements, which could negatively affect the performance of the SS. On the other hand, load forecasting is extremely important since they help electricity companies to make ...

In order to efficiently improve the prediction accuracy, two load forecasting model based on fuzzy theory are presented, which are fuzzy clustering model and improved fuzzy regression analysis model .The method of fuzzy clustering is used to divide the area by the similar feature of load increasing. The new division is promising to improve the result of evident degree of clustering index to pow...

Short term electrical load forecasting is critical in ensuring reliability and operational efficiency for electrical systems. With an influx of monitoring data and the growing technical complexity of the grid, there is a great interest and need for accurate forecasting in electricity planning. Our project uses a curated electric load dataset from Kaggle and evaluates the performance of several ...

Electric load forecasting is a key to the efficient management of power supply system. Load forecasting, which involves estimation of future load according to the previous load data. This paper presents a pragmatic methodology for short term load forecasting (STLF) using proposed hybrid method of wavelet transform (WT) and artificial neural network (ANN). It is a two stage prediction system whi...

The quality of short term load forecasting can improve the efficiency of planning and operation of electric utilities. Artificial Neural Networks (ANNs) are employed for nonlinear short term load forecasting owing to their powerful nonlinear mapping capabilities. At present, there is no systematic methodology for optimal design and training of an artificial neural network. One has often to reso...

Medium-term load forecasting is a useful tool for the maintenance planning of grids and as a market research of electric energy. In this work medium-term load forecasting methods are developed, the most forgotten time scaling process in the load forecasting bibliography. These methods will be applied to the peninsular Spanish monthly energy consumption. Methods traditionally employed with this ...

Accurate load forecasting is an important issue for the reliable and efficient operation of a power system. This study presents a hybrid algorithm that combines similar days (SD) selection, empirical mode decomposition (EMD), and long short-term memory (LSTM) neural networks to construct a prediction model (i.e., SD-EMD-LSTM) for short-term load forecasting. The extreme gradient boosting-based ...

As accurate Short Term Load Forecasting (STLF) is very important for improvement of the management performance of the electric industry, various short term loads forecasting methods have been developed. This paper addresses an issue of the optimal design of a neural network based short term load forecaster. A new hybrid evolutionary algorithm combining the Particle Swarm Optimization (PSO) algo...