Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban ...Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban high temperature continues, demand for power is increased and results in the degradation of electricity reserves. The current trend in climate change, regardless of the summer and winter power demand, is likely to have much effect on the power demand. Thus, sensitivity to electricity consumption in urban areas due to climate change was researched. The results show that, 1） the basic unit of the sensitivity to electricity consumption in the target areas is 1.25-1.58W/（m2.℃）; 2） The maximum sensitivity is recorded at around 8：00 pm in the area crowded with commercial and business area. And in the business area, electricity consumption load is even from 9：00 am to 6：00 pm.展开更多
GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some...GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.展开更多
基金Project(NRF-20110030631) supported by the National Research Foundation of Korea Grant funded by the Korean Government
文摘Recently, urban high temperature phenomenon has become a problem which results from human activities, the increase in energy consumption, and land-cover change in urban areas. As extremely hot weather caused by urban high temperature continues, demand for power is increased and results in the degradation of electricity reserves. The current trend in climate change, regardless of the summer and winter power demand, is likely to have much effect on the power demand. Thus, sensitivity to electricity consumption in urban areas due to climate change was researched. The results show that, 1） the basic unit of the sensitivity to electricity consumption in the target areas is 1.25-1.58W/（m2.℃）; 2） The maximum sensitivity is recorded at around 8：00 pm in the area crowded with commercial and business area. And in the business area, electricity consumption load is even from 9：00 am to 6：00 pm.
基金supported by the National Natural Science Foundation of China(72171116,71671090)the Fundamental Research Funds for the Central Universities(NP2020022)+1 种基金the Key Research Projects of Humanities and Social Sciences in Anhui Education Department(SK2021A1018)Qinglan Project for Excellent Youth or Middlea ged Academic Leaders in Jiangsu Province,China.
文摘GM(1,1)models have been widely used in various fields due to their high performance in time series prediction.However,some hypotheses of the existing GM(1,1)model family may reduce their prediction performance in some cases.To solve this problem,this paper proposes a self-adaptive GM(1,1)model,termed as SAGM(1,1)model,which aims to solve the defects of the existing GM(1,1)model family by deleting their modeling hypothesis.Moreover,a novel multi-parameter simultaneous optimization scheme based on firefly algorithm is proposed,the proposed multi-parameter optimization scheme adopts machine learning ideas,takes all adjustable parameters of SAGM(1,1)model as input variables,and trains it with firefly algorithm.And Sobol’sensitivity indices are applied to study global sensitivity of SAGM(1,1)model parameters,which provides an important reference for model parameter calibration.Finally,forecasting capability of SAGM(1,1)model is illustrated by Anhui electricity consumption dataset.Results show that prediction accuracy of SAGM(1,1)model is significantly better than other models,and it is shown that the proposed approach enhances the prediction performance of GM(1,1)model significantly.
