IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308 _________________________________________________________________________________________ Volume: 03 Issue: 04 | Apr-2014, Available @ http://www.ijret.org 322 FUZZY LOGIC METHODOLOGY FOR SHORT TERM LOAD FORECASTING Patel Parth Manoj 1 , Ashish Pravinchandra Shah 2 1 M.E. Student, Electrical Engineering Department, Parul Institute of Engineering and Technology, Limda Waghodia, Vadodara, India 2 Assistant Professor, Electrical Engineering Department, Parul Institute of Engineering and Technology, Limda Waghodia, Vadodara, India Abstract Load forecasting is an important component for power system energy management system. Precise load forecasting helps the electric utility to make unit commitment decisions, reduce spinning reserve capacity and schedule device maintenance plan properly and it also reduces the generation cost and increases reliability of power systems. In this work, a fuzzy logic approach for short term load forecasting is attempted. Time, temperature and similar previous day load are used as the independent variables for short ter m load forecasting. Based on the time, temperature and similar previous day load, fuzzy rule base are prepared using mamdani implication, which are eventually used for the short term load forecasting. MATLAB SIMULINK software is used here in this work. For the short term load forecasting, load data from the specific area load dispatch center is considered. Keywords: Load forecasting, short term load forecasting, Fuzzy logic, Fuzzy inference system. ---------------------------------------------------------------------***--------------------------------------------------------------------- 1. INTRODUCTION The prime duty of any utility is to provide reliable power to customers. Customer load demand in electric distribution systems is subject to change because human activities follow daily, weekly, and monthly cycles. The load demand is usually higher during the daytime and in evening, when industrial loads are high, lights are on, and lower in late evening and early morning when most of the population is asleep. Estimating the distribution system load expected at some time in the future is an important task in order to meet exactly any network load at whatever time it occurs. The estimation of future active loads at various load buses ahead of actual load occurrence is known as load forecasting. If it is done inappropriately, then the direct effect is on the planning for the future load and the result is the difference of the load that will develop from the planning done for the same, and eventually the entire planning process is at risk. Therefore, load forecast plays a crucial role in all aspects of planning, operation, and control of an electric power system. It is an important task for operating a power system reliably and economically. So, the need and relevance of forecasting load for an electric utility has become an important issue in the recent past. It is not only important for distribution or power system planning but also for evaluating the cost effectiveness of investing in the new technology and the strategy for its propagation. However, in the deregulated market, load forecasting is of utmost importance. As the utility supply and consumer demand is fluctuating and the change in weather conditions, energy prices increases by a factor of ten or more during peak load, load forecasting is vitally important for utilities. Short-term load forecasting is a helping tool to estimate load flows and to anticipate for the overloading. Network reliability increases if the overloading effects are eliminated in time. Also, it reduces rates of equipment failures and blackouts. Load forecasting is however not an easy task to perform. First, because the load on consumer side is complex and shows several levels of seasonality: the load at a given hour is dependent on the load at the previous hour as well as on the load at the same hour on the previous day, and also on the load at the same hour on the day with the same quantity in the previous week. Secondly, there are many important externally affecting variables that should be considered, particularly weather related variables [1]. There are large varieties of mathematical methods that are used for load forecasting, the development and improvements of suitable mathematical tools will lead to the development of more accurate load forecasting techniques. The accuracy of load forecasting depends on the load forecasting techniques used as well as on the accuracy of forecasted weather parameters such as temperature, humidity etc. As per the recent trends artificial intelligence methods are the most pronounced for the STLF. From different artificial intelligence methods, fuzzy logic and artificial neural network are the most used. Among the two methods fuzzy logic for STLF is gaining