Due to the electricity market deregulation and integration of renewable resources, electrical load forecasting is becoming increasingly important for the Chinese government in recent years. The electric load cannot be exactly predicted only by a single model, because the short-term electric load is disturbed by several external factors, leading to the characteristics of volatility and instability. To end this, this paper proposes a hybrid model based on wavelet transform (WT) and least squares support vector machine (LSSVM), which is optimized by an improved cuckoo search (CS). To improve the accuracy of prediction, the WT is used to eliminate the high frequency components of the previous day's load data. Additional, the Gauss disturbance is applied to the process of establishing new solutions based on CS to improve the convergence speed and search ability. Finally, the parameters of the LSSVM model are optimized by using the improved cuckoo search. According to the research outcome, the result of the implementation demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
Electric power is a kind of unstorable energy concerning the national welfare and the people's livelihood, the stability of which is attracting more and more attention. Because the short-term power load is always interfered by various external factors with the characteristics like high volatility and instability, a single model is not suitable for short-term load forecasting due to low accuracy. In order to solve this problem, this paper proposes a new model based on wavelet transform and the least squares support vector machine (LSSVM) which is optimized by fruit fly algorithm (FOA) for short-term load forecasting. Wavelet transform is used to remove error points and enhance the stability of the data. Fruit fly algorithm is applied to optimize the parameters of LSSVM, avoiding the randomness and inaccuracy to parameters setting. The result of implementation of short-term load forecasting demonstrates that the hybrid model can be used in the short-term forecasting of the power system.
With the deterioration of the global greenhouse effect, the study of carbon dioxide emissions has received more and more international attention and accurate prediction of carbon dioxide emissions is also important for the formulation of reasonable energy-saving emission reduction measures. In this paper, the genetic algorithm is used to optimize the initial connection weights and thresholds of the traditional back propagation neural network (BPNN) which can give full play to the advantages of the genetic algorithm's global search capacity and BPNN's local search. The data of Hebei province in China during the period 1978–2012 are selected to carry out the carbon dioxide emissions prediction with the established model. In the view of the choice of input variables, the coal consumption, gross domestic product, total population, and urbanization level are examined by Pearson coefficient test. Auto correlation and partial correlation are applied to analyze the inner relationships between the historic carbon dioxide emissions, thus to select the input variables of BPNN. Besides, in order to verify the validity of the built model, the residual auto correlation and partial correlation are done upon the training set. The prediction results suggest the proposed model outperforms the compared models.
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