Applications of Genetic Algorithms to Load Forecasting Problem

Evolutionary programs are gaining popularity in many engineering and scientific applications due to their enormous advantages such as adaptability, ability to handle non-linear, ill-defined and probabilistic problems. Specific reference to genetic algorithms (Gas), some parameters that influence the convergence to the optimal value are the population size (popsize), the crossover probability (Pc) and the mutation propability (Pm). Normally these values are prescribed initially and do not vary during the execution of the program, although these parameters greatly affect the performance of GA.

The present chapter deals with the development of an improved genetic algorithm (IGA) by introducing a variation in the values of the parameters like population size (popsize), the crossover probability (Pc) and the mutation propability (Pm). The aim of this variation is to minimize the convergence time. This work presents a method of dynamically varying the parameters of operation of the GA program using fuzzy state theory (FST) so that the final convergence is obtained in a shorter time.

Also, in this chapter a function has been developed and optimized for long-term load forecasting problem using IGA. This technique does not require any previous assumption of a function for load forecasting, further, it does not need any functional relationship between dependent and independent variables. The results obtained by this technique are compared with the data available from central electricity authority (CEA), India to demonstrate the effectiveness of the proposed technique.