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In the paper is proposed a new neuro-wavelet based approach for the problem of short term load forecasting. The implemented neuro-wavelet based algorithm combines the potential of two soft computing techniques. The strength over other approaches appeared in literature is that firstly the hourly power load data are wavelet processed and then provided as input to an RNN. The obtained simulation results...
Load Forecasting plays a critical role in the management, scheduling and dispatching operations in power systems, and it concerns the prediction of energy demand in different time spans. In future electric grids, to achieve a greater control and flexibility than in actual electric grids, a reliable forecasting of load demand could help to avoid dispatch problems given by unexpected loads, and give...
Accurate short term load forecasting (STLF) is a prerequisite for proper generation scheduling and reliable operation of power utilities. Conventional methods of STLF, suffer from the disadvantages such as lack of ability to accurately model the weather parameters affecting the load, lack of robustness for representing weekends and public holidays and of being computation intensive. Application of...
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...
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