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Cyber-Phys. Syst."],"published-print":{"date-parts":[[2017,10,31]]},"abstract":"<jats:p>\n            The popularity of rooftop solar for homes is rapidly growing. However, accurately forecasting solar generation is critical to fully exploiting the benefits of locally generated solar energy. In this article, we present two machine-learning techniques to predict solar power from publicly available weather forecasts. We use these techniques to develop SolarCast, a cloud-based web service that automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a \u201cblack box\u201d approach that requires only (1) a site\u2019s geographic location and (2) a\n            <jats:italic>minimal<\/jats:italic>\n            amount of historical generation data. Since we intend SolarCast for small rooftop deployments, it does not require detailed site- and panel-specific information, which owners may not know, but instead automatically learns these parameters for each site.\n          <\/jats:p>\n          <jats:p>We evaluate the accuracy of SolarCast\u2019s different algorithms on two publicly available datasets, each containing over 100 rooftop deployments with a variety of attributes (e.g., climate, tilt, orientation, etc.). We show that SolarCast learns a more accurate model using much less data (\u223c1 month) than prior SVM-based approaches, which require \u223c3 months of data. SolarCast also provides a programmatic API, enabling developers to integrate its predictions into energy efficiency applications. Finally, we present two case studies of using SolarCast to demonstrate how real-world applications can leverage its predictions. We first evaluate a \u201csunny\u201d load scheduler, which schedules a dryer\u2019s energy usage to maximally align with a home\u2019s solar generation. We then evaluate a smart solar-powered charging station, which can optimally charge the maximum number of electric vehicles (EVs) on a given day. Our results indicate that a representative home is capable of reducing its grid demand up to 40% by providing a modest amount of flexibility (of \u223c5 hours) in the dryer\u2019s start time with opportunistic load scheduling. Further, our charging station uses SolarCast to provide EV owners the amount of energy they can expect to receive from solar energy sources.<\/jats:p>","DOI":"10.1145\/3004056","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T11:49:04Z","timestamp":1503575344000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["A Cloud-Based Black-Box Solar Predictor for Smart Homes"],"prefix":"10.1145","volume":"1","author":[{"given":"Srinivasan","family":"Iyengar","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Navin","family":"Sharma","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Irwin","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashant","family":"Shenoy","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst, Amherst, MA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Krithi","family":"Ramamritham","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Bombay, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,8,24]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2014.6846755"},{"key":"e_1_2_1_2_1","unstructured":"Apple. 2013. 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