Being an energy forecaster, I am genuinely interested in meteorology. I even recruited a master student who was a practicing meteorologist in Hawaii (see the blog post about Ying Chen). The more energy forecasting projects I conduct, the more I appreciate the value of weather data. In GEFCom2014, the top 1 place of the solar track was a team of meteorologists from Australia, who completely dominated the track. In GEFCom2017, the top 1 place of the final match was a team of meteorologists from Japan. I truly believe that the energy forecasting community can better leverage meteorology than what we do today. Here is an article about two use cases of weather data for energy analytics. In fact we merged two papers into one by removing the sophisticated mathematics and statistics to keep the story readable to a broad audience. The IEEE Power and Energy Society is so kind to offer the open access to this paper, so that people can read it for free.
Citation
Jonathan Black, Alex Hofmann, Tao Hong, Joseph Roberts, and Pu Wang, "Weather data for energy analytics: from modeling outages and reliability indices to simulating distributed photovoltaic fleets," IEEE Power and Energy Magazine, vol.16, no.3, pp 43-53, May-June 2018. (Open Access; IEEE Xplore)
Abstract
Weather impacts virtually all facets of our daily life. As a result, many business sectors are affected by weather conditions, and the power industry is no exception. Weather is a major influencer on system reliability and a key driver of both power supply and demand. In this article, we will demonstrate novel uses of weather data for energy analytics via two utility applications. We first use easily accessible weather data together with regression analysis to model distribution outages and construct a probabilistic view of reliability indices that helps reveal a utility’s reliability trend. We then use high-resolution, commercial-grade weather data to develop realistic simulations of anticipated behind-the-meter photovoltaic (PV) fleets
Citation
Jonathan Black, Alex Hofmann, Tao Hong, Joseph Roberts, and Pu Wang, "Weather data for energy analytics: from modeling outages and reliability indices to simulating distributed photovoltaic fleets," IEEE Power and Energy Magazine, vol.16, no.3, pp 43-53, May-June 2018. (Open Access; IEEE Xplore)
Weather Data for Energy Analytics
From Modeling Outages and Reliability Indices to Simulating Distributed Photovoltaic Fleets
Jonathan Black, Alex Hofmann, Tao Hong, Joseph Roberts, and Pu Wang
Weather impacts virtually all facets of our daily life. As a result, many business sectors are affected by weather conditions, and the power industry is no exception. Weather is a major influencer on system reliability and a key driver of both power supply and demand. In this article, we will demonstrate novel uses of weather data for energy analytics via two utility applications. We first use easily accessible weather data together with regression analysis to model distribution outages and construct a probabilistic view of reliability indices that helps reveal a utility’s reliability trend. We then use high-resolution, commercial-grade weather data to develop realistic simulations of anticipated behind-the-meter photovoltaic (PV) fleets
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