This work presents a novel approach to address challenging and still unsolved problem of neural network based load forecasting systems, that despite the significant results reached in terms prediction error reduction, lack suitable indications regarding sample-wise trustworthiness their predictions. The present is framed on Bayesian Mixture Density Networks, enhancing mapping capabilities netwo...

Forecasting of electricity demand has become one of the most important areas of research in the electric power industry, as it is a critical component of cost-efficient power system management and planning. In this context, accurate and robust load forecasting is supposed to play a key role in reducing generation costs, and deals with the reliability of the power system. However, due to demand ...

The Wavelet Neural Network (WNN) is widely used in power load forecasting. In view that the traditional WNN easily falls into the local minimum and has unstable forecast results, a new power load forecasting model of combining the AdaBoost algorithm with WNN was put forward to improve the forecasting accuracy and generalization ability. Firstly, the method performed the pre-treatment for the hi...

Two new computing models, namely a fuzzy expert system and a hybrid neural network-fuzzy expert system for time series forecasting of electric load, are presented in this paper. The fuzzy-logic-based expert system utilizes the historical relationship between load and dry-bulb temperature, and predicts electric loads fairly accurately, 1-24 h ahead. In the case of the hybrid neural network-fuzzy...

In this paper, we propose a neural network approach to forecast AM/PM Jordan electric power load curves based on several parameters (temperature, date and the status of the day). The proposed method has an advantage of dealing with not only the nonlinear part of load curve but also with rapid temperature change of forecasted day, weekend and special day features. The proposed neural network is ...

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...

Providing accurate load forecast to electric utility corporations is essential in order to reduce their operational costs and increase profits. Hence, training set selection is an important preprocessing step which has to be considered in practice in order to increase the accuracy of load forecasts. The usage of mutual information (MI) has been recently proposed in regression tasks, mostly for ...

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...

Smart grid is the main development goal of future power grid while the short-term load forecasting is the significant premise of making management, power supply and trading plan in market circumstance. The forecasting accuracy directly determined the safety and economy of electric system. Support Vector Machines (SVM), as the new machine learning method, has applied successfully to short-termed...