To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided ...To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.展开更多
A systematic study on the electrical load forecasting for large-scale iron and steel companies was made. After analyzing the electrical load＇s characteristics, an algorithm framework for the load forecasting in iron ...A systematic study on the electrical load forecasting for large-scale iron and steel companies was made. After analyzing the electrical load＇s characteristics, an algorithm framework for the load forecasting in iron and steel complex was formulated based on model combination and scheme filtration. The algorithm features data quality self- adaptation, convenient forecasting model extension, easy practical application, etc. , and has been successfully applied in Baoshan Iron and Steel Co Ltd, Shanghai, China, resulting in great economic benefit.展开更多
A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadr...A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.展开更多
This paper proposes a hybrid short-term load forecasting method,which is based on the fuzzy combination weights as well as the empirical mode decomposition process(FCW-EMD),and support vector machine optimized via the...This paper proposes a hybrid short-term load forecasting method,which is based on the fuzzy combination weights as well as the empirical mode decomposition process(FCW-EMD),and support vector machine optimized via the Bat algorithm as well as the Kalman filtering process(KF-BA-SVM).The subjective weight is presented as a new theory and is applied to capture the inherent correlation effectively among hourly loads.Based on the proposed objective weights and subjective weights,the fuzzy combination weights theory(FCW)-a new similar day selection theory is presented,which improves the accuracy of the similar day selection,and correspondingly,makes the original data for EMD processing decrease dramatically.BA is introduced to optimize parameters of the SVM model for further improving the forecasting accuracy.Using the decomposed load series via empirical model decomposition(EMD)as inputs to SVM and further correcting the output of SVM via KF,a hybrid FCW-EMD and KF-BA-SVM short-term load forecasting method is established.Numerical case studies on the load forecasting of a transformer substation in south China show that the proposed hybrid forecasting model outperforms other forecasting methods and effectively improves the prediction accuracy.展开更多
文摘To solve the medium and long term power load forecasting problem,the combination forecasting method is further expanded and a weighted combination forecasting model for power load is put forward.This model is divided into two stages which are forecasting model selection and weighted combination forecasting.Based on Markov chain conversion and cloud model,the forecasting model selection is implanted and several outstanding models are selected for the combination forecasting.For the weighted combination forecasting,a fuzzy scale joint evaluation method is proposed to determine the weight of selected forecasting model.The percentage error and mean absolute percentage error of weighted combination forecasting result of the power consumption in a certain area of China are 0.7439%and 0.3198%,respectively,while the maximum values of these two indexes of single forecasting models are 5.2278%and 1.9497%.It shows that the forecasting indexes of proposed model are improved significantly compared with the single forecasting models.
基金Item Sponsored by National Natural Science Foundation of China (59937150 ,60274054),863 High Tech Development Plan ofChina (2001AA413910) and National Outstanding Young Investigator Grant (6970025)
文摘A systematic study on the electrical load forecasting for large-scale iron and steel companies was made. After analyzing the electrical load＇s characteristics, an algorithm framework for the load forecasting in iron and steel complex was formulated based on model combination and scheme filtration. The algorithm features data quality self- adaptation, convenient forecasting model extension, easy practical application, etc. , and has been successfully applied in Baoshan Iron and Steel Co Ltd, Shanghai, China, resulting in great economic benefit.
文摘A new kind of combining forecasting model based on the generalized weighted functional proportional mean is proposed and the parameter estimation method of its weighting coefficients by means of the algorithm of quadratic programming is given. This model has extensive representation. It is a new kind of aggregative method of group forecasting. By taking the suitable combining form of the forecasting models and seeking the optimal parameter, the optimal combining form can be obtained and the forecasting accuracy can be improved. The effectiveness of this model is demonstrated by an example.
文摘This paper proposes a hybrid short-term load forecasting method,which is based on the fuzzy combination weights as well as the empirical mode decomposition process(FCW-EMD),and support vector machine optimized via the Bat algorithm as well as the Kalman filtering process(KF-BA-SVM).The subjective weight is presented as a new theory and is applied to capture the inherent correlation effectively among hourly loads.Based on the proposed objective weights and subjective weights,the fuzzy combination weights theory(FCW)-a new similar day selection theory is presented,which improves the accuracy of the similar day selection,and correspondingly,makes the original data for EMD processing decrease dramatically.BA is introduced to optimize parameters of the SVM model for further improving the forecasting accuracy.Using the decomposed load series via empirical model decomposition(EMD)as inputs to SVM and further correcting the output of SVM via KF,a hybrid FCW-EMD and KF-BA-SVM short-term load forecasting method is established.Numerical case studies on the load forecasting of a transformer substation in south China show that the proposed hybrid forecasting model outperforms other forecasting methods and effectively improves the prediction accuracy.
