Abstract
The classic models used to predict the behavior of photovoltaic systems, which are based on the physical process of the solar cell, are limited to defining the analytical equation to obtain its electrical parameter. In this paper, we evaluate several machine learning models to nowcast the behavior and energy production of a photovoltaic (PV) system in conjunction with ambient data provided by IoT environmental devices. We have evaluated the estimation of output power generation by human-crafted features with multiple temporal windows and deep learning approaches to obtain comparative results regarding the analytical models of PV systems in terms of error metrics and learning time. The ambient data and ground truth of energy production have been collected in a photovoltaic system with IoT capabilities developed within the Opera Digital Platform under the UniVer Project, which has been deployed for 20 years in the Campus of the University of Jaén (Spain). Machine learning models offer improved results compared with the state-of-the-art analytical model, with significant differences in learning time and performance. The use of multiple temporal windows is shown as a suitable tool for modeling temporal features to improve performance.
Highlights
Photovoltaic (PV) power generation has been shown to be a successful technology with a remarkable level of maturity with more than 500 GW of solar photovoltaic (PV) power installed all over the world at the end of 2018, in some cases running for several years, and with a forecast of 1 TW of total power being generated by 2022, most of it in large PV plants
Two different approaches for machine learning models have been proposed: (i) human-crafted features and multiple temporal windows and (ii) deep learning for automatic feature extraction and learning. Both approaches present encouraging performance in nowcasting output energy generation in the photovoltaic system based on data collected from ambient sensors; we highlight the model based on human-crafted features and multiple temporal windows for its lower learning time and best results
An IoT module and data-driven models to nowcast output energy generation integrated in the Opera Digital Platform project have been described
Summary
Photovoltaic (PV) power generation has been shown to be a successful technology with a remarkable level of maturity with more than 500 GW of solar photovoltaic (PV) power installed all over the world at the end of 2018, in some cases running for several years, and with a forecast of 1 TW of total power being generated by 2022, most of it in large PV plants. Data represent a key asset in this PV management area, since they enable us to model the standard behavior of the system and to monitor its performance compared with the expected output determined by the model. This monitoring, when applied promptly and comprehensively, taking account of all the factors that may impact performance, enables early damage and fault detection, which allows operation and maintenance actions to maximize the up-time and efficiency of PV plants. Leveraging the latest software advances in machine learning, a different approach can be taken by using regressors to build models, which learn from data on the actual behavior of the system during a relevant period of time and use Sensors 2020, 20, 4224; doi:10.3390/s20154224 www.mdpi.com/journal/sensors
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