Research On Power Load Forecasting Method Based On Intelligent Computing

Short-term load forecasting of distribution network is an important daily work for the dispatching and operating department of power system. The level of forecast precision directly impacts on the safety, economy and quality of power supply of power system operation. Mid-long term load forecasting is the prerequisite and foundation of distribution network planning, which accuracy directly counts for the level and quality of layout scheme, and it has instructive significance to reasonably layout distribution network. It is also one of the important contents of accomplishing power system management modernization.In this paper, many kinds of Intelligent Computing methods were synthetically adopted. As various aspects of the predicting work being the clues, the input parameters of daily maximum load forecasting to short-term load forecasting neural network were selected, and the models and methods of short-term load multi-variable regularized regression chaos partial forecasting, mid-long combination forecasting and spatial load forecasting are established, which enriched the methods of load forecasting of distribution network. The primary research was as follows:(1) As for the input parameter selection of short-term load forecasting, a property reduction arithmetic based on concept lattice was introduced, and input parameter selecting arithmetic of neural network was also put forward. The property was reduced, and we chosed property parameter that has good relativity to forecasting load as input of forecasting model of neural network. It completely ensured the rationality of input parameters of the forecasting model, and provides guarantee on the forecasting Accuracy of the neural network model, moreover, it could effectively reduce the amount of calculation for the neural network model.(2)Distribution system is a nonlinear system, showing some certain chaotic behaviors. The multivariate time series were constructed, in order to reconstruct the more accurate phase space, and to enhance the precision of short-term load forecasting. We chosed the effective temperature factors with the greatest impact on the load. Firstly, time delay and embedding dimension were confirmed with the methods of mutual information and the minimum predicting error. Secondly, according to reconstruction parameters, the phase space of short-term load multivariate time series was reconstructed. Thirdly, aiming at few neighboring points in the partial predicting method that can not satisfy least-square estimate condition, multivariate time series chaos partial forecasting model based on the regularized regression was presented. Moreover, such model was carried on in practical power load forecast, and the forecasting accuracy was enhanced.(3)Based on analyzing all kinds of forecasting methods for mid-long term load amounts of distribution network, the changeable combination forecasting method based on hierarchy structure was presented. Firstly, the weight determining is the most important issue. We decomposed it as hierarchy structure, and a recursion order hierarchy structure was formed. The evaluation indicator of models was synthesized considered. Secondly, the variance-covariance optimum combined forecasting method was selected from fitting error indexes and the grey association analysis method was selected from developing relative indexes, which separately determined the relative weight from the solution level to the principal level. Thirdly, adopting the entropy method, the relative weights of two indexes as fitting error and developing relative were confirmed, and then the final combination weight of the combination forecasting method was obtained. The conception of dimensional information was brought in the combination forecasting, and the changeable combination forecasting was achieved.(4)Based on analyzing all kinds of forecasting methods for spatial load of distribution network, the spatial load forecasting method for distribution network based on concept lattice and cellular automaton was presented. In the dynamic process of residential land-using types, the theory of cellular automaton was brought in to achieve the dynamic simulation of each residential land-using type. According to the actual state of development in planning areas, the conversion and iterative time of cellular automaton was confirmed, which made predicting outcomes be more reasonably and be in accordance with the actual developing discipline of spatial load in planning areas. In the process of obtaining the decision rules of residential land-using, the theory of concept lattice was introduced. Adopting the attribute reduction algorithm in the theory of concept lattice, all kinds of condition attributes affecting land-using types of planning areas were reduced. The decision rules of future residential land-using types were obtained.In the whole process of spatial load forecasting, the idea of the Land Usage Method for"from top to bottom"load distribution was adopted. With the load amounts forecasting in planning areas, its outcomes were assigned to each area, thus the spatial load forecasting in planning areas was accomplished.