Abstract
The Ontario electrical grid is sized to meet peak electricity load. A reduction in peak load would allow deferring large infrastructural costs of additional power plants, thereby lowering generation cost and electricity prices. Proposed solutions for peak load reduction include demand response and storage. Both these solutions require accurate prediction of a home's peak and mean load. Existing work has focused only on mean load prediction. We find that these methods exhibit high error when predicting peak load. Moreover, a home's historic peak load and occupancy is a better predictor of peak load than observable physical characteristics such as temperature and season. We explore the use of Seasonal Auto Regressive Moving Average (SARMA) for peak load prediction and find that it has 30% lower root mean square error than best known prior methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
