28-08-2017, 05:00 PM
Forecasting the load is essential for planning and operation in energy management. It improves the efficient and reliable operation of a power system's energy. The energy supplied by utilities satisfies the load plus the energy lost in the system is ensured by this tool. Since in the energy system the next day's power generation must be programmed every day. Short-term load forecasting (STLF) is a daily task required for energy dispatch. The forecast of the short-term load is essential for the commitment of the unit, the economic allocation of the generation, the maintenance schedules. This paper presents a solution methodology that uses fuzzy logic for short - term load prediction. The fuzzy logic approach is implemented in weather-sensitive data and historical load data to forecast the load. The proposed methodology uses fuzzy reasoning decision rules that capture nonlinear relationships between inputs and outputs. Input data includes historical load and time data such as temperature, humidity and wind speed. Jaipur Vidyut Nigam hourly freight data is used for training and testing which are collected from the State Shipping Freight and Communication Center, Rajasthan Vidyut Parasaran Nigam. The results of the predicted load are obtained from the fuzzy logic model using the triangular membership function.