In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationshi...In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationship is identified by using Empirical Mode Decomposition(EMD)and linear regression analysis together with outlier detection.EMD is a data processing tool that can divide original data into several Intrinsic Mode Functions(IMFs)with a lower frequency residue.By applying the data mining technique-Mahalanobis distance measurement,some outliers from real-time gas consumption and temperature data points are detected,which are excluded from the data sets to ensure accuracy.Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis.By comparing such indices on realtime data and EMD processed data,the weather-sensitive part of gas demand is identified.The methods are implemented on a local energy system and the results reveal that the outcome after EMD presents a higher level of correlation between the gas load and ambient temperature,compared to the results from directly using the real-time gas load and temperature data.展开更多
In this paper,the short-,medium-,and long-term effects of the COVID-19 pandemic on the Italian power system,particularly electricity consumption behavior and electricity market prices,are investigated by defining vari...In this paper,the short-,medium-,and long-term effects of the COVID-19 pandemic on the Italian power system,particularly electricity consumption behavior and electricity market prices,are investigated by defining various metrics.The investigation reveals that COVID-19 lockdown caused a drop in load consumption and,consequently,a decrement in day-ahead market prices and an increase in ancillary service prices.展开更多
文摘In order to understand energy consumption and ensure precise load prediction,it is essential to identify the variation of gas consumption in response to ambient temperature change outdoor.In this paper,the relationship is identified by using Empirical Mode Decomposition(EMD)and linear regression analysis together with outlier detection.EMD is a data processing tool that can divide original data into several Intrinsic Mode Functions(IMFs)with a lower frequency residue.By applying the data mining technique-Mahalanobis distance measurement,some outliers from real-time gas consumption and temperature data points are detected,which are excluded from the data sets to ensure accuracy.Correlation coefficients between the gas load and ambient temperature are calculated and denoted as an important index to quantify their relationship through regression analysis.By comparing such indices on realtime data and EMD processed data,the weather-sensitive part of gas demand is identified.The methods are implemented on a local energy system and the results reveal that the outcome after EMD presents a higher level of correlation between the gas load and ambient temperature,compared to the results from directly using the real-time gas load and temperature data.
文摘In this paper,the short-,medium-,and long-term effects of the COVID-19 pandemic on the Italian power system,particularly electricity consumption behavior and electricity market prices,are investigated by defining various metrics.The investigation reveals that COVID-19 lockdown caused a drop in load consumption and,consequently,a decrement in day-ahead market prices and an increase in ancillary service prices.
