Application of Machine Learning Algorithm to Forecast Load and Development of a Battery Control Algorithm to Optimize PV System Performance in Phoenix, Arizona
|Abstract||The students of Arizona State University, under the mentorship of Dr George Karady, have been collaborating with Salt River Project (SRP), a major power utility in the state of Arizona, trying to study and optimize a battery-supported grid-tied rooftop Photovoltaic (PV) system, sold by a commercial vendor. SRP believes this system has the potential to satisfy the needs of its customers, who opt for utilizing solar power to partially satisfy their power needs.
An important part of this elaborate project is the development of a new load forecasting algorithm and a better control strategy for the optimized utilization of the storage system. The built-in algorithm of this commercial unit uses simple forecasting and battery control strategies. ... (more)
|Contributor||Hariharan, Aashiek (Author) / Karady, George G (Advisor) / Heydt, Gerald Thomas (Committee member) / Qin, Jiangchao (Committee member) / Allee, David R (Committee member) / Arizona State University (Publisher)|
|Subject||Electrical engineering / Battery Control Algorithm / Distributed Generation / Load Forecasting / Machine Learning / PV Forecasting / Rooftop PV Systems|
|Note||Masters Thesis Electrical Engineering 2018|
|Collaborating Institutions||Graduate College / ASU Library|
|Additional Formats||MODS / OAI Dublin Core / RIS|