Short-term Power Load Forecasting Research Articles

Short-term power load forecasting using grey correlation contest modeling

Power load has the characteristic of nonlinear fluctuation and random growth. Aiming at the drawback that the forecasting accuracy of general GM(1,1) model goes down when there is a greater load mutation, this paper proposes a new grey model with grey correlation contest for short-term power load forecasting. In order to cover the impact of various certain and uncertain factors in climate and society on the model as fully as possible, original series are selected from different viewpoints to construct different forecasting strategies. By making full use of the characteristic that GM(1,1) model can give a perfect forecasting result in the smooth rise and drop phase of power load, and the feature that there are several peaks and valleys within daily power load, the predicted day is divided into several smooth segments for separate forecasting. Finally, the different forecasting strategies are implemented respectively in the different segments through grey correlation contest, so as to avoid the error amplification resulted from the improper choice of initial condition. A practical application verifies that, compared with the existing grey forecasting models, the proposed model is a stable and feasible forecasting model with a higher forecasting accuracy.

Optimizing of SVM with Hybrid PSO and Genetic Algorithm in Power Load Forecasting

In this paper, we propose Hybrid Particle Swarm Optimization (HPSO) with genetic algorithm(GA) mutation to optimize the SVM forecasting model. In the process of doing so, we first use HPSO with genetic algorithm to make pretreatment of the data. PSO with GA model is a method for finding a solution of stochastic global optimizer based on swarm intelligence. Using the interaction of particles, PSOGA model searches the solution space intelligently, this will find out the best one and reduce the redundant information. The PSOGA-SVM method proposed in this paper is based on the global optimization of PSOGA and local accurate searching of SVM. And to prove the effectiveness of the model, single SVM algorithm and BP network was used to compare with the result of PSOGA-SVM. The results show that the model is effective and highly accurate in the forecasting of short-term power load than the other models. The root-mean-square relative error (RMSRE) of new model is only 1.82%, the SVM model and BP network is 2.43%, 4.10% separately.

Optimization of support vector machine power load forecasting model based on data mining and Lyapunov exponents

According to the chaotic and non-linear characters of power load data, the time series matrix is established with the theory of phase-space reconstruction, and then Lyapunov exponents with chaotic time series are computed to determine the time delay and the embedding dimension. Due to different features of the data, data mining algorithm is conducted to classify the data into different groups. Redundant information is eliminated by the advantage of data mining technology, and the historical loads that have highly similar features with the forecasting day are searched by the system. As a result, the training data can be decreased and the computing speed can also be improved when constructing support vector machine (SVM) model. Then, SVM algorithm is used to predict power load with parameters that get in pretreatment. In order to prove the effectiveness of the new model, the calculation with data mining SVM algorithm is compared with that of single SVM and back propagation network. It can be seen that the new DSVM algorithm effectively improves the forecast accuracy by 0.75%, 1.10% and 1.73% compared with SVM for two random dimensions of 11-dimension, 14-dimension and BP network, respectively. This indicates that the DSVM gains perfect improvement effect in the short-term power load forecasting.

A New Short-term Power Load Forecasting Model Based on Chaotic Time Series and SVM

This paper presents a model for power load forecasting using support vector machine and chaotic time series. The new model can make more accurate prediction. In the past few years, along with power system privatization and deregulation, accurate forecast of electricity load has received increasing attention. According to the chaotic and non-linear characters of power load data, the model of support vector machines (SVM) based on chaotic time series has been established. The time series matrix has also been established according to the theory of phase-space reconstruction. The Lyapunov exponents, one important component of chaotic time series, are used to determine time delay and embedding dimension, the decisive parameters for SVM. Then support vector machines algorithm is used to predict power load. In order to prove the rationality of chosen dimension, another two random dimensions are selected to compare with the calculated dimension. And to prove the effectiveness of the model, BP algorithm is used to compare with the results of SVM. Findings show that the model is effective and highly accurate in the forecasting of short-term power load. It means that the model combined with SVM and chaotic time series learning system have more advantage than other models.

Short-term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation

This article presents the results of a study aimed at the development of a system for short-term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This study indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs when tested on the testing data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 591–605, 2005.

Combining a neural network with a rule-based expert system approach for short-term power load forecasting in Taiwan

A back-propagation neural network with the output provided by a rule-based expert system is designed for short-term power load forecasting. To demonstrate that the inclusion of the prediction from a rule-based expert system of a power system would improve the predictive capability of the network, load forecasting is performed on the Taiwan power system. The hourly load for one typical day was evaluated and, in that case, the inclusion of the rule-based expert system prediction significantly improved the neural network's prediction of power load. Moreover, the proposed combined approach converges much faster than the conventional neural network and the rule-based expert system method. Extensive studies were performed on the robustness of the built network model by using different specified censoring time. The prediction intervals of future power load series are also provided, to evaluate the prediction efficiency of the neural network model.