Real-time Load Prediction with High Velocity Smart Home Data Stream
This work addresses load balancing in electrical micro-grids for smart-home participants, but it is incremental as it applies existing methods to a new dataset.
The paper tackles real-time energy load prediction for households in local markets using a new three-year, high-resolution smart-home dataset, and evaluates various machine learning methods for short-term load prediction, reporting their performance and computational requirements.
This paper addresses the use of smart-home sensor streams for continuous prediction of energy loads of individual households which participate as an agent in local markets. We introduces a new device level energy consumption dataset recorded over three years wich includes high resolution energy measurements from electrical devices collected within a pilot program. Using data from that pilot, we analyze the applicability of various machine learning mechanisms for continuous load prediction. Specifically, we address short-term load prediction that is required for load balancing in electrical micro-grids. We report on the prediction performance and the computational requirements of a broad range of prediction mechanisms. Furthermore we present an architecture and experimental evaluation when this prediction is applied in the stream.