Meteorological Factor Multivariate Models Affecting Solar Power Prediction using Long Short-term Memory
Nam-Rye Son1, Seung-Hak Yang2
1NamRye Son, Dept. Of Information and Communication Engineering, Honam University, Gwang-Ju Korea.
2SeungHak Yang*, Dept. Of Electrical Engineering, Honam University, Gwang-Ju, Korea.
Manuscript received on January 13, 2020. | Revised Manuscript received on January 20, 2020. | Manuscript published on February 10, 2020. | PP: 142-147 | Volume-9 Issue-4, February 2020. | Retrieval Number: D1172029420/2020©BEIESP | DOI: 10.35940/ijitee.D1172.029420
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Solar power systems have been recently installed in buildings to efficiently manage their energy consumption and production in them. Because electrical energy is produced and consumed simultaneously owing to its physical nature, it is necessary to predict the exact solar power necessary to maintain a stable power supply. To manage the building energy management system (BEMS) effectively, this paper proposes 6 models (solar radiation, sunlight, humidity, temperature, cloud cover, wind speed) and compares the performances of these models. Through this comparison, we solved the traditional long short-term memory (LSTM) problem and proposed a new LSTM. It was determined that the meteorological factors for forecasting solar power varied by season. The performance was shown in order of solar radiation, sunshine, wind speed, temperature, cloudiness and humidity at annual average. Additionally, the proposed LSTM performed better than the traditional LSTM.
Keywords: Solar Power Prediction, Meteorological Factor, Long Short-term Memory, Building Energy Management System
Scope of the Article: Building Energy