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|Fuzzy Set Classified Neural Network for Short-Term Load Forecasting
|Jain, Sanjay Kumar
|Short Term Load Forecasting;Fuzzy Logic;Fuzzy set based classification;Neural Network
|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. Besides playing a key role in reducing the generation cost, it is also essential to the reliability of power systems. The system operators use the load forecasting result as a basis of off-line network analysis to determine if the system might be vulnerable. If so, corrective actions should be prepared, such as load shedding, power purchases and bringing peaking units on line. Load forecasting plays an important role in power system planning, operation and control. Forecasting means estimating active loads at various load buses ahead of actual load occurrence. Planning and operational applications of load forecasting requires a certain ‘lead time’ also called forecasting intervals. On the basis of lead time, load forecasts can be divided into four categories: very short-term forecasts, short-term forecasts, medium-term forecasts and long-term forecasts. The forecasts for different time horizons are important for different operations within a utility company. Since in power systems the next days’ power generation must be scheduled everyday, day-ahead short-term load forecasting (STLF) is a necessary daily task for power dispatch. Its accuracy affects the economic operation and reliability of the system greatly. Under prediction of STLF leads to insufficient reserve capacity preparation and, in turn, increases the operating cost by using expensive peaking units. On the other hand, over prediction of STLF leads to the unnecessarily large reserve capacity, which is also related to high operating cost. In this work, a Fuzzy Set Classified Neural Network Approach for Short Term Load Forecasting is attempted and implemented using Matlab 6.5. First of all, training data is classified using Fuzzy Set Based Classification Method. Temperature data is classified into five fuzzy sets (Very Cold, Cold, Normal, Hot and Very Hot). Relative Humidity is classified into four fuzzy sets (Very Dry, Dry, Humid and Very Humid). Day Type is classified into four fuzzy sets (Post-Holiday, Weekday, Pre-Holiday and Holiday). So, depending upon the temperature, relative humidity and day type, data is classified into eighty classes. After the classification, the neural network is trained for various classes using the historical data. The multilayer neural network structure has been used and the training is imparted using back propagation algorithm. Thereafter, the trained neural network is used for load forecasting. The work presented here is divided into three steps: 1. Fuzzy Set Based Classification: Classification of training data using Fuzzy Set Based Classification Technique and also identify classes for all the 24 hours of the day for which the load is to be forecasted. 2. Training of Neural Network: Training of the neural network for each hour of each day for which the load is to be forecasted using the training data of that particular class to which that hour belongs. 3. Short term load forecasting: Forecasting of hourly load using trained neural network. The test cases studied in this work to validate the accuracy of the proposed technique are as:- • Case-1: Summer and Post-Holiday • Case-2: Summer and Pre-Holiday • Case-3: Winter and Weekday • Case-4: Summer and Holiday • Case-5: Rainy season and Weekday
|M.E. (Power System and Electric Drives)
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