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Artificial Neural Networks (ANN) have lately received much attention, and a great number of papers have reported successful experiments and practical tests. This paper presents the development of a simple ANN topology for load forecasting model with much improved accuracy for the Regional Power Control Centre of Saudi Electricity Company. The proposed system is based on optimising the initial random...
This paper presents the development of an Artificial Neural Networks and Particle Swarm Optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the Regional Power Control Center of Saudi Electricity Company, Western Operation Area (SEC-WOA). Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land...
Aiming at improving the accuracy and speed of short-term load forecasting (STLF), the proposed BCC-LS-SVM model is presented, among which bacterial colony chemotaxis (BCC) optimization algorithm is used to determine hyper-parameters of least squares support vector machine (LS-SVM). BCC is a novel category of bionic algorithm, which takes advantage of the bacterium's reaction to chemoattractants to...
This paper presents artificial neural networks and particle swarm optimization (ANN-PSO) based short-term load forecasting model with improved generalization technique for the regional power control center of Saudi Electricity Company, Western Operation Area (SEC-WOA) of Saudi Arabia. Weather, load demand, wind speed, wind direction, heat, sunlight, etc. are quite different in a desert land than other...
This paper presents a new BP neural network (BP NN) forecast model named IPSO-BP forecast model that is based on an improved particle swarm optimization (IPSO). The improved PSO employs parameter with crossover operator and mutations operator to significantly improve the performance of the original PSO algorithm in global search and fine-tuning of the solutions. This study uses the IPSO algorithm...
Short-term load forecasting in power system is necessary for management and control of power system. A new method for short-term load forecasting was presented based on neural networks optimized by genetic algorithm (GA) is proposed in this paper, short-term load forecasting model for power system was setup as sample sets for Elman neural network (Elman NN), with GA's optimizing and Elman NN's dynamic...
This paper optimizes the wavelet neural networks with genetic algorithms which has the optimization of the overall search capabilities, and establishes the model of wavelet neural networks based on genetic algorithms. It overcomes the shortcomings of BP neural network for their own, and it can get higher accuracy and faster convergence. The examples also show that the model can improve forecast accuracy...
As the development of energy market and the interest for new energy recourses such as wind energy and solar energy, energy management system of distributed generation (DG) becomes significant for the stability and economic operation of the DG system. This paper introduced a basic structure of DG system and illustrated the principle of power forecasting using neutral network. Finally a novel energy...
With the development of electronic industry, accurate load forecasting of the future electricity demand plays an important role in regional or national power system strategy management. Electricity load forecasting is difficult due to the nonlinearity of its influencing factors. Support vector machine (SVM) have been successfully applied to solve nonlinear regression and time series problems. However,...
The development of wind generation has rapidly progressed over the last decade. With the advance in wind turbine technologies, wind energy has become competitive with other fuel-based generation resources. The fluctuation of wind, however, makes it difficult to optimize the use of wind power generation. Current practice ignores the possible available capacity of the wind generation during the unit...
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