M.A. Thesis:

The short - term prediction with the gray wolf method

Abstract Electricity can't be stored on a large scale, and the management of electricity generation and distribution must plan, operate and invest optimally based on adapting supply to electricity demand. In future planning of a power system, load prediction is of particular importance and its error rate should be reduced as much as possible. In this dissertation, first, various load forecasting methods were introduced and analyzed. Short-term forecasting is very important for planning energy purchases and efficient use of the power system. Short-term forecasting of the load is difficult due to the lack of accurate knowledge of consumer participation, given the demand-side response. The various methods proposed for load forecasting have so far not been able to track the demand-side response. Therefore, the short-term load prediction method was introduced using the gray wolf algorithm. Modeling based on this method was applied to Iran and US data and simulated and compared. The results show that the proposed method is more effective than other methods of load prediction and is done with less error. key words Short-term load forecasting, intelligent algorithms, gray wolf

Keywords: gray wolf