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 paper, 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, which automatically generates models that provide customized site-specific predictions of solar generation. SolarCast utilizes a ``black box'' approach that requires only i) a site's geographic location and ii) a minimal 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. We evaluate the accuracy of SolarCast's different algorithms on two publicly available datasets, each containing over one hundred 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 (~1 month) than prior SVM-based approaches, which require ~3 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 ``sunny" load scheduler, which schedules a dryer's energy usage to maximally align with a home's 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 ~5 hours) in the dryer's 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.
In this paper, we perform a comprehensive survey to the technical aspects related to the implementation of demand response and smart buildings. Specifically, we discuss various smart loads such as heating, ventilating, and air-conditioning (HVAC) systems and plug-in electric vehicles (PEVs), the power architecture with multi-bus characteristics, different control algorithms such as the hybrid centralized and decentralized control and the distributed coordination among buildings, the communication technologies and network architectures, and the potential cyber-physical security issues and possible mechanisms for enhancing the system security at both cyber and physical layers. The current status of the demand response in United States, Europe, Japan, and China is reviewed, and the benefits, costs, and challenges of implementing and operating demand response and smart buildings are also discussed.