Load Prediction in Smart Grid Networks

Provided by: University of Bahrain
Topic: Networking
Format: PDF
Efficient forecasting and load prediction for maintaining the accurate DR (Demand Response) ratio is a key factor in implementing and deploying the smart-grid networks. There are a plethora of techniques and models suggested by forecasters over the decades, the most accurate and feasible being-artificial neural networks, linear regression technique and the curve fitting algorithm. Researchers have demonstrated extreme zeal and effort in developing algorithms which could derive the best efficiency, thus saving excess production than demand. For example, the work described in the paper puts forward the prediction values to be at an accuracy of around 95%. A hybrid algorithm has been presented in this paper, which has been practically proved to have a forecasting efficiency much higher than the conventional methods.

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