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.

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

Grey model using dimensional information update technology always contains the latest information from sample data, it guarantees the accuracy of mid-long term load forecasting. Semi-parametric regression model which combines the advantages of parametric model and nonparametric model, it can fully reflect the complexity and uncertainties of the load change. This paper put forward an improved method which combines grey model with semi-parametric regression model by time-varying weight for load forecasting, the proposed method would make full use of data information and consider its inherent regularity completely, which makes prediction more realistic. At last, a comparison of the error has been made between the single model and the combination model. The test example results show that this method has higher precision.

The variable weight combination load forecasting based on grey model and semi-parametric Regression Model

Based on the analysis limitation of GM(1,1) gray model, genetic algorithm is introduced into the model of GM(1,1), a new gray forecasting method is proposed to forecast the mid-long term load of power systems. Simulated calculation results demonstrate that it is an accurate and efficient method.

A New Method for Mid-long Term Load Forecasting of Power Systems

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A New Method for Mid-long Term Load Forecasting of Power Systems