Based on the fast algorithm of meteorological satellite guide wind vector tracing, cloud motion wind vector is calculated. According to the different characteristics of cloud motion wind field and sounding wind field,...Based on the fast algorithm of meteorological satellite guide wind vector tracing, cloud motion wind vector is calculated. According to the different characteristics of cloud motion wind field and sounding wind field, a method which fuses conventional data with unconventional data based on variation principle is presented. The fundamental is constructing a cost function that makes the value approach conventional data and the gradient approach unconventional data. Using this method, the conventional wind and the cloud motion wind are fused. The fused wind field has high resolu- tion. Its wind direction approaches cloud motion wind which indicates move direction of the synoptic system, and its velocity approaches conventional wind which indicates move velocity of the synoptic system. The wind field data are used for short-time forecast of severe convective weather location, which gets a good result.展开更多
In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hour...In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.展开更多
文摘Based on the fast algorithm of meteorological satellite guide wind vector tracing, cloud motion wind vector is calculated. According to the different characteristics of cloud motion wind field and sounding wind field, a method which fuses conventional data with unconventional data based on variation principle is presented. The fundamental is constructing a cost function that makes the value approach conventional data and the gradient approach unconventional data. Using this method, the conventional wind and the cloud motion wind are fused. The fused wind field has high resolu- tion. Its wind direction approaches cloud motion wind which indicates move direction of the synoptic system, and its velocity approaches conventional wind which indicates move velocity of the synoptic system. The wind field data are used for short-time forecast of severe convective weather location, which gets a good result.
基金funded in part by Grant No.DF-091-135-1441 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.
