Abstract
The decision-making of power generation enterprises, power supply enterprises, and power consumers can be affected by forecasting the price of electricity. There are many irrelevant samples and features in big data, which often lead to low forecasting accuracy and high time-cost. Therefore, this paper proposes a forecasting framework based on big data processing, which selects a small quantity of data to achieve accurate forecasting while reducing the time-cost. First, the sample selection based on grey correlation analysis (GCA) is established to eliminate useless samples from the periodicity. Second, the feature selection based on GCA is established considering the feature classification and the temporal correlation features to further eliminate useless features. Third, principal component analysis is applied to reduce the noise among the data. Then, combined with a differential evolution algorithm (DE), a support-vector machine (SVM) is applied to forecast the price. Finally, the proposed framework is applied to the New England electricity market to forecast the short-term electricity price. The results show that, compared with DE-SVM without data processing, the forecasting accuracy is improved from 81.68% to 91.44%, and the time-cost is decreased from 35,074 s to 1,809 s which shows that the proposed method and model can provide a valuable tool for data processing and forecasting.
Highlights
In the power market, the electricity price is an essential element because it influences the behaviour of power generation enterprises, power supply enterprises, and other buyers [1]
Framework of Forecasting. e time-cost and forecasting accuracy of differential evolution algorithm (DE)-support-vector machine (SVM) will be affected when the irrelevant data in the samples and features of the electricity price are applied to the forecasting process. erefore, this paper proposes feature classification and establishes three models of data processing to select and extract the valid samples and features, which reduces the time-cost and improves the accuracy
Parameter Settings. e parameters in the framework are set as follows: (i) sample selection based on grey correlation analysis (GCA): the distinguishing factors ξ1 0.5, the control threshold μ1 0.983; (ii) feature selection based on GCA: the distinguishing factors ξ2 0.5, the control threshold μ2 0.64; (iii) DE-SVM: population size NP 50, the maximum number of iterations is 50, the scaling factor range is [0.2, 0.8], the crossover probability is 0.5, the insensitivity coefficient of SVM is set to 0.01, and the change range of c and σ is set to [0.01, 10]
Summary
The electricity price is an essential element because it influences the behaviour of power generation enterprises, power supply enterprises, and other buyers [1]. Erefore, how to extract and mine useful information from the electricity price and its influencing factors are extremely important for accurate and timely forecasting. The methods of forecasting include traditional forecasting methods [2,3,4,5,6] and intelligence methods [7,8,9,10,11,12] Among these methods, support-vector machine (SVM) has higher generalization ability and good robustness, so it is widely applied to forecast electricity price. In [15], a least-squares SVM, combining radial basis function (RBF) and universal SVM kernel function, was applied to forecast electricity price
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