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In this paper, an evaluation theory of hybrid model for short-term electricity load forecasting is presented using simple soft-technique of predicting data. A model that integrates fuzzy system with neural network database is demonstrated and eventually compared with a traditional statistical method of linear regression. Power load forecasting errors especially for weekends, which is much higher than...
The increased integration of wind power into the electric grid, as it occurs today in Portugal, poses new challenges due to its intermittency and volatility. Wind power forecasting plays a key role in tackling these challenges. A novel hybrid approach, combining wavelet transform, particle swarm optimization, and an adaptive-network-based fuzzy inference system, is proposed in this paper for short-term...
Energy crisis, global warming and depletion of ozone layer are the major factors looming the world today. Adequate utilization of renewable energy sources like wind, solar, biomass etc. prove to be the only alternative. As a result, these industries are rapidly gaining significance. The wind power industry is very promising and it is necessary for the wind farm power prediction to be exact. Prediction...
When neural networks are used to forecast short-term power load, it can learn the experience by training and generate mapping rules, but these rules are not directly understood in the network. By using the method of integrating neural networks and fuzzy logic, neural networks only settle historical load information. Moreover, fuzzy logic considers the factors which have great effect to load varying,...
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