Delgado Muñoz, Carlos Julian

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    Weather variables forecasting to reduce their impact on photovoltaic systems
    (2024-03-23) Delgado Muñoz, Carlos Julian; O'Neill Carrillo, Efraín; College of Engineering; Andrade Rengifo, Fabio; Manian, Vidya; Department of Electrical and Computer Engineering; Patarroyo Montenegro, Juan
    Photovoltaic (PV) power generation forecasting is an important research topic, aiming to mitigate the 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, Ambient weather, and NSRDB datasets. The proposed workflow demonstrated superior performance compared to the results obtained by other 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 as the output variable.