Day-ahead electricity price forecasting via the application of artificial neural network based models

A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review

Mid-term electricity market clearing price forecasting: A hybrid LSSVM and ARMAX approach

Mid-term electricity market clearing price forecasting: A multiple SVM approach

Mid-term electricity market clearing price forecasting utilizing hybrid support vector machine and auto-regressive moving average with external input

Accurate electricity price forecasting can provide crucial information for electricity market participants to make reasonable competing strategies. Support vector machine (SVM) is a novel algorithm based on statistical learning theory, which has greater generalization ability, and is superior to the empirical risk minimization principle as adopted by traditional neural networks. However, its generalization performance depends on a good setting of the training parameters C, for the nonlinear SVM. In the study, support vector machine trained by genetic algorithm (GA-SVM) is adopted to forecast electricity price, in which GA is used to select parameters of SVM. National electricity price data in China from 1996 to 2007 are used to study the forecasting performance of the GA-SVM model. The experimental results show that GA-SVM algorithm has better prediction accuracy than radial basis function neural network (RBFNN).

Electricity Price Forecasting Based on Support Vector Machine Trained by Genetic Algorithm

Mediumand long-term runoff forecasting is closely related to the generation capacity forecasting of cascade hydropower stations, which is of great significance to power plants when arranging production plans and assisting market decisions. In order to improve the accuracy of runoff forecasting, an attempt was made to use random forest regression (RFR) to model the mediumand long-term runoff forecasting and to further make a verification based on the actual monthly runoff data of Mupo and Chuntangba stations. By comparison with the forecast results attained through a support vector machine (SVM) and an integrated autoregressive moving average model (IARMA), the results showed that the RFR model had the lowest mean square error (MSE) among the three methods. In addition, the coefficients of determination R of the RFR for the two stations increased by 0.0261 and 0.0295 compared with the SVM model, and the R rose by 0.1134 and 0.1332 compared with the IARMA model. The comparison of the three methods showed that the RFR had higher forecasting accuracy as well as stronger reliability and practicability than the IARMA model and the SVM model, so the RFR provided a new idea and method for the study of runoff forecasting.

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Medium- and long-term runoff forecasting based on a random forest regression model

Much energy is dissipated when large equipment moves slowly. Generally, equipment scheduling at construction site is supposed to minimize equipment slowdown and deadhead moving. Table methods are always adopted to optimize transfer sequence, but the feasible solution is well disappointing. For the acceptable solution relevant to task points in construction equipment scheduling, transfer table is divided into four regions. After proper augmentation and deflation, the acceptable solution evolves into global coordinating solution of construction scheduling, which contributes to minimizing slowdown and deadhead mileages. This method has been verified in practical engineering and is a significant reference on decision making of construction equipment scheduling.

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Ma Guangwen

Papers

Electricity Price Forecasting Based on Support Vector Machine Trained by Genetic Algorithm

Medium- and long-term runoff forecasting based on a random forest regression model

Research for Global Coordinating Method of Large Equipment Scheduling in Construction Site

Multi-Objective Optimization Based Joint Dispatch Model of Wind-Solar-Hydro-Thermal Pumped Storage