Photovoltaic Power Generation Forecasting with Hidden Markov Model and Long Short-Term Memory in MISO and SISO Configurations

Carlos J. Delgado, Estefanía Alfaro-Mejía, Vidya Manian, Efrain O’Neill-Carrillo and Fabio Andrade ()
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Carlos J. Delgado: Electrical and Computer Engineering Department, University of Puerto Rico, Mayagüez, PR 00680, USA
Estefanía Alfaro-Mejía: Electrical and Computer Engineering Department, University of Puerto Rico, Mayagüez, PR 00680, USA
Vidya Manian: Electrical and Computer Engineering Department, University of Puerto Rico, Mayagüez, PR 00680, USA
Efrain O’Neill-Carrillo: Electrical and Computer Engineering Department, University of Puerto Rico, Mayagüez, PR 00680, USA
Fabio Andrade: Electrical and Computer Engineering Department, University of Puerto Rico, Mayagüez, PR 00680, USA

Energies, 2024, vol. 17, issue 3, 1-18

Abstract: Photovoltaic (PV) power generation forecasting is an important research topic, aiming to mitigate variability caused by weather conditions and improve power generation planning. Climate factors, including solar irradiance, temperature, and cloud cover, influence the energy conversion achieved by PV systems. Long-term weather forecasting improves PV power generation planning, while short-term forecasting enhances control methods, such as managing ramp rates. The stochastic nature of weather variables poses a challenge for linear regression methods. Consequently, advanced, state-of-the-art machine learning (ML) approaches capable of handling non-linear data, such as long short-term memory (LSTM), have emerged. This paper introduces the implementation of a multivariate machine learning model to forecast PV power generation, considering multiple weather variables. A deep learning solution was implemented to analyze weather variables in a short time horizon. Utilizing a hidden Markov model for data preprocessing, an LSTM model was trained using the Alice Spring dataset provided by DKA Solar Center. The proposed workflow demonstrated superior performance compared to the results obtained by state-of-the-art methods, including support vector machine, radiation classification coordinate with LSTM (RCC-LSTM), and ESNCNN specifically concerning the proposed multi-input single-output LSTM model. This improvement is attributed to incorporating input features such as active power, temperature, humidity, horizontal and diffuse irradiance, and wind direction, with active power serving as the output variable. The proposed workflow achieved a mean square error (MSE) of 2.17 × 10 − 7 , a root mean square error (RMSE) of 4.65 × 10 − 4 , and a mean absolute error (MAE) of 4.04 × 10 − 4 .

Keywords: photovoltaic systems; irradiance; machine learning; forecasting; LSTM; electric grid; hidden Markov models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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