TY - GEN
T1 - Very Short-Term PV Power Prediction Using Machine Learning Models
AU - Javadi, Masoud
AU - Naderi, Soheil
AU - Liang, Xiaodong
AU - Gong, Yuzhong
AU - Chung, Chi Yung
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022/9
Y1 - 2022/9
N2 - Due to the intermittency of solar photovoltaic (PV) power and fast fluctuations in the PV output power, very short-term PV power prediction is of paramount importance for efficient control of resources and units such as loads and energy storage systems and market regulation. As PV power is volatile and highly nonlinear, data-driven machine learning models are developed to predict PV power for a very short-term horizon. In this study, 10 previous samples (i.e., 50 minutes of data) are used as features to predict PV power for the current time and 5 next time periods (i.e., 25 minutes). Four machine learning techniques including Linear Regression (LR), Random Forest Regression (RFR), Multi-Layer Perceptron (MLP) neural network, and long short term memory (LSTM) are utilized in this study. Metrics including the coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) have been used to evaluate the performance of the developed machine learning models. Simulation results on a one-year dataset with a sampling resolution of five minutes indicate that the prediction accuracy of the proposed tuned machine learning methods is high and acceptable. The optimized RFR is found to be the best method in terms of computational performance and accuracy.
AB - Due to the intermittency of solar photovoltaic (PV) power and fast fluctuations in the PV output power, very short-term PV power prediction is of paramount importance for efficient control of resources and units such as loads and energy storage systems and market regulation. As PV power is volatile and highly nonlinear, data-driven machine learning models are developed to predict PV power for a very short-term horizon. In this study, 10 previous samples (i.e., 50 minutes of data) are used as features to predict PV power for the current time and 5 next time periods (i.e., 25 minutes). Four machine learning techniques including Linear Regression (LR), Random Forest Regression (RFR), Multi-Layer Perceptron (MLP) neural network, and long short term memory (LSTM) are utilized in this study. Metrics including the coefficient of determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) have been used to evaluate the performance of the developed machine learning models. Simulation results on a one-year dataset with a sampling resolution of five minutes indicate that the prediction accuracy of the proposed tuned machine learning methods is high and acceptable. The optimized RFR is found to be the best method in terms of computational performance and accuracy.
KW - deep learning
KW - machine learning
KW - PV power prediction
KW - regression
UR - http://www.scopus.com/inward/record.url?scp=85141000406&partnerID=8YFLogxK
U2 - 10.1109/CCECE49351.2022.9918269
DO - 10.1109/CCECE49351.2022.9918269
M3 - Conference article published in proceeding or book
AN - SCOPUS:85141000406
T3 - Canadian Conference on Electrical and Computer Engineering
SP - 55
EP - 59
BT - 2022 35th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 35th IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2022
Y2 - 18 September 2022 through 20 September 2022
ER -
