Research On Swarm Intelligence Algorithms And Power Load Forecasting

Load forecasting is very significant and useful in the construction and operation of power systems. Prediction accuracy directly affects the safety operation of power systems and economic benefits .In this paper, first of all, swarm intelligence algorithms was be studied. In recent years it has been widely used in load forecasting. Next, a predictive model that combined with swarm intelligence algorithm was studied. Through existing algorithms and models which have been improved, the new prediction model will be obtained and used in practical load forecasting, getting good results. The main content of this article is as follow:1. Research on two typical swarm intelligence algorithms: particle swarm optimization(PSO) and ant colony optimization algorithm(ACO). Both the advantages and disadvantages are gained from analysis and comparison of their results for different function optimization,. Simulated annealing algorithm and absorb advantages of ant colony algorithm,Improved particle swarm optimization (IPSO)and hybrid algorithm(HA) have been proposed. Improved Swarm Intelligence Algorithm, the standard particle swarm algorithm and ant colony algorithms, would be compared for different function optimization. The results show that the improved swarm intelligence algorithm has more advantages and effectiveness.2. Combined the swarm intelligence algorithms and neural network to get a establishment of new forecasting mode. Respectively, the improved swarm intelligence algorithm combined with BP & Elman neural network. The improved swarm intelligence algorithms optimized neural network weights and thresholds established optimization algorithm based on swarm intelligence neural network prediction model: PSOBPNN,PSOENN,IPSOBPNN,IPSOENN,HABPNN and HAENN. Through the classical chaotic sequence simulation, it has been verified that the algorithm based on swarm intelligence neural network prediction model has faster convergence rate and higher forecast accuracy than the traditional one.3. Using swarm intelligence algorithm instead of the traditional least squares method to directly solve the gray model parameters. This avoids the background value of the Gray model solution process due to the error caused by improper values. Through the middle and long term load forecast, it has been verified that the gray prediction model based on swarm intelligence algorithm has better prediction accuracy and the broader scope of application than the traditional one.4. Short-term load forecasting method is a hot topic of current research, establishing methods of training samples is closely related with the prediction accuracy. Step multi-step prediction and forecasting would be studied. According to the analysis of multi-step prediction step prediction, a new prediction method combined the advantages of the two methods would be proposed. Under the new forecasting method, training samples would be established. Neural network model is used to complete the city's actual short-term load forecasting and the results show that the new forecasting method has better prediction accuracy while it's combined with HA.5. Analysis and comparison of fall and summer load characteristics , prove such a rule: the small correlations of power load and temperature in the fall and crucial relevance of power load and temperature in the summer. Power load forecasting during the fall do not need to use the temperature factor but get good results. Power load forecasting during the summer, the temperature factor need to be used in order to obtain better results.6. Sampling signal preprocessing for improving prediction accuracy is of special significance.Through best wavelet packet decomposition, the complex load sequence into several simple regularity of the load weight. Each of these components using new forecasting method to predict the load in the low frequency components of the summer forecast, the introduction of real-time temperature factor, the final reconstructed sequence of the final load forecast results, to improve the prediction accuracy. An example of simulation would verify the feasibility of the method.