Electric Power System Combination Forecasting Based On Swarm Intelligence And Fusion Algorithm

Load forecasting is important for power system planning and design, system operation and management. It is the guarantee for the reliability and economic operation of electric power system. The intrinsic features of the electricity load are nonlinear, uncertainty, and randomness. For a long period scholars dedicated themselves to the research work of load forecasting technique and many effective methods have been proposed. However, the rules of load forecasting have become more complex with the development of electricity market and the traditional prediction way establish single model and has poor adaptive abilities. Traditional load forecasting method which using a single model to predict in complex conditions has obviously become less capable for the prediction task and may not be able to get the satisfactory results. Hence, the conflict between the complexity of the problem and the limitations of solving methods is more prominent. The combination Forecasting has become a focus for the research which can be integrated more information and enhanced the adaptive capability. The ideas for the combination can be summed up in two main aspects:Firstly, improve the performance of prediction model by the combination with the optimal algorithm. The algorithm is used to optimize the prediction models for better performance in forecasting. Secondly, the combination of different single prediction models by given each model certain weight. The combination model can integrate relevant information given by each model and improve the accuracy of load forecasting.In this paper, we deeply study the theories and methods of the load forecasting by adopting the virtual forecasting technique, swarm intelligence algorithm and combination forecasting theory, and obtain a series of conclusions which have theoretical and practical value. The main research and innovative results are as follows:(1) According to the optimization problem of the prediction model, describes the process of the standard particle swarm algorithm optimization and then proposed the improved particle swarm optimization(FAPSO). In particle swarm optimization, the search process must include explore and develop. The inertia weight needs larger step for global search at first, then the inertia weight requires gradually reduce for the local search. Take the changes of weight followed with the particles'fitness change. Map the changes in inertia weight by the exponential function. The disturbance for extreme value also added in the algorithm. The convergence analysis and the corresponding function tests for this self-adaptive particle swarm algorithm showed that the performance of FAPSO is better than that of the traditional particle swarm algorithm. (2) This paper proposes particle swarm optimization algorithm with weight linearity reduced (LWPSO) combined with Radial Basis Function (RBF) neural network model for short term load forecasting. The global search capability of particle swarm is used to optimize the weight of Radial Basis Function neural network. Implement the process by using LabVIEW for its powerful array processing capabilities and intuitionist way of programming. Simulation results show that the prediction accuracy and stability are better than that of the traditional one.(3) In view of the sample limited problem of load forecasting, the paper discussed support vector machine load forecasting model based on the improved particle swarm (FAPSO) optimization. The impact of the parameters for support vector machine is analyzed and then proposed FAPSO algorithm to optimize the relevant parameters of support vector machine which is found to overcome the shortcoming of the traditional way. The simulation shows that this method has faster convergence speed, and better performance compared with the traditional support vector machine parameter determination method.(4) The global convergence capability of simulated annealing (SA) and the efficiency of particle swarm search are combined as fusion algorithm in the way of self-study. Proposed the fusion algorithm NSAPSO with the disturbance for extreme value added in. SA has the ability of choosing to receive the bad solution in order to have the ability to leap out the local optimization. The simulation shows that this algorithm has better performance for global convergence.(5) In view of the problem of the combination of different single prediction models, support vector machines based on NSAPSO is proposed for nonlinear combined model. The model avoid the complex way of solving the weighting coefficient in the traditional combination way and it can fit with the requirement of non linear, variable weight requirement. It has a good performance and practice value for short term load forecasting.