Abstract
Abstract
Based on the 15min-by-15min power load in Anhui Province from 2016 to 2018 and the daily meteorological data in the same period, the response of power load to climate change is analyzed. On the basis of calculating weather load rate, CDD, and HDD, multivariate regression analysis, time series linear regression analysis establishes a multivariate regression model and a time-fixing effect model of electroculation temperature response, respectively. The results show that the change of temperature has a significant effect on power load. Humidity also has a significant impact on load changes in months with warming demand. CDD and HDD play a positive role in the growth of inter-provincial power load, and the elasticity coefficient of CDD is less than that of HDD. The basic results have passed the robustness test.
Reference12 articles.
1. Climate change impacts on electricity demand in the State of New South Wales, Australia;Ahmed;Applied Energy,2012
2. Evaluating regional climate-electricity demand nexus: A composite Bayesian predictive framework;Mukherjee;Applied Energy,2019
3. Climate-change impacts on electricity demands at a metropolitan scale: A case study of Guangzhou, China;Zheng;Applied Energy,2020
4. Short-term load forecasting considering weather factors;Yi;East China Electric Power ( in Chinese),2013
5. Sensitivity analysis of meteorological factors to summer maximum power load;Ren;Meteor Mon(in Chinese),2020
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