Electricity load forecasting is a key task in the planning and operation of power systems and electricity markets, and its importance increases with the advent of smart grids. In this paper, we present AWNN, a new approach for very short-term load forecasting. AWNN decomposes the complex electricity load data into components with different frequencies that are predicted separately. It uses an a...

Environmental monitoring is the most popular application of wireless sensor networks (WSNs). At present, WSNs have been used for a number of applications such as soil moisture monitoring, solar radiation mapping, aquatic monitoring, glacial control and climate change, and forest fire alarm. The ability to place autonomous and low cost nodes in large harsh environments without communication infr...

The performance of model predictive control (MPC) for energy management in hybrid electric vehicles (HEVS) is strongly dependent on the projected future driving profile. This paper proposes a novel velocity forecasting method based on artificial neural networks (ANN). The objective is to improve the fuel economy of a power-split HEV in a nonlinear MPC framework. In this study, no telemetry or o...

Electricity demand forecasting model based on single algorithm at least have two problems related to local optima and computational cost. We consider to utilised the hybrid real value genetic algorithm and extended Nelder-Mead to solved local optima and reduced the number of iteration. The model is known as the hybrid Real-Value GA and Extended Nelder-Mead (RVGA-ENM). The GA has been enhanced t...

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...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

A Hidden Markov Model (HMM) is used to classify an out of sample observation vector into either of two regimes. This leads to a procedure for making probability forecasts for changes of regimes in a time series, i.e. for turning points. Instead of maximizing a likelihood, the model is estimated with respect to known past regimes. This makes it possible to perform feature extraction and estimati...

Long-term demand forecasting presents the first step in planning and developing future generation, transmission and distribution facilities. One of the primary tasks of an electric utility accurately predicts load demand requirements at all times, especially for long-term. Based on the outcome of such forecasts, utilities coordinate their resources to meet the forecasted demand using a least-co...

We apply machine-learning techniques to construct nonlinear nonparametric forecasting models of consumer credit risk. By combining customer transactions and credit bureau data from January 2005 to April 2009 for a sample of a major commercial bank’s customers, we are able to construct out-of-sample forecasts that significantly improve the classification rates of credit-card-holder delinquencies...

The integration of wind farms in power networks has become an important problem. As electricity cannot be preserved because of the highest cost of storage, electricity production must following market demand, necessarily. Short-long term wind forecasting over different time steps is becoming an important process for the management of wind farms. Time series modelling of wind speeds is based on ...