Research on Long Term Load Forecasting Based on Improved Genetic Neural Network

Considering the features of long term load forecasting are complicated, this paper proposes a generic neural network model that is able to adapt to and learn from amount of non-linear or imprecise rules, so it is a model with highly robustness. For avoiding the inflexibility of the generic neural network itself, many experiences and opinions of experts are introduced during the use, so that a comprehensive effect of different factors that influence the power load can be reflected. The generic algorithm is able to search precisely at global scope, and the neural network is able to fit well at local scope, both of which are chosen together by this paper, i.e. the generic neural network algorithm. In the simulation training of the model, data from 1990 to 2007 of 16 indexes are used, and experience and opinions of national regulation factors of primary, secondary and tertiary industries that are provided by experts are also followed, and the long term load forecasting is carried out by rolling annual extrapolation.