Application of Machine Learning Algorithm to Forecast Load and Development of a Battery Control Algorithm to Optimize PV System Performance in Phoenix, Arizona
| Autor: | P.E Joel Dickinson, George G. Karady, Aashiek Hariharan |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Battery (electricity)
Computer science business.industry 020208 electrical & electronic engineering Photovoltaic system 02 engineering and technology 021001 nanoscience & nanotechnology Machine learning computer.software_genre Power (physics) Work (electrical) Installation Computer data storage 0202 electrical engineering electronic engineering information engineering Artificial intelligence State (computer science) 0210 nano-technology Rooftop photovoltaic power station business computer Algorithm |
| Zdroj: | 2018 North American Power Symposium (NAPS). |
| DOI: | 10.1109/naps.2018.8600594 |
| Popis: | The paper presents the results of the research work funded by Salt River Project Agricultural Improvement and Power District (SRP) on maximizing the economic benefits to customers installing residential rooftop PV systems in SRP territory. The optimized discharge of the battery power which would help in the reduction of Demand Charge paid by the customer was the primary goal. Machine Learning algorithms were utilized as a better load forecasting technique to the ones already in place. The improved battery discharge algorithm would also reduce the battery charge-discharge cycles (cycling aging) thus, improving the battery life. The tests were performed in the state of Arizona, on a residential rooftop grid-tied PV with storage system installed at the Tempe campus of the Arizona State University. |
| Databáze: | OpenAIRE |
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