Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting
Solar photovoltaic (PV) systems are pivotal and transformative technologies at the forefront of the global shift toward sustainable energy solutions. The primary challenge in solar energy production lies in the volatility and intermittency of PV system power generation, primarily due to unpredictabl...
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| Médium: | E-kniha |
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| Jazyk: | English |
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MDPI - Multidisciplinary Digital Publishing Institute
2024
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| ISBN: | 3725800677, 9783725800681, 3725800685, 9783725800674 |
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| Abstract | Solar photovoltaic (PV) systems are pivotal and transformative technologies at the forefront of the global shift toward sustainable energy solutions. The primary challenge in solar energy production lies in the volatility and intermittency of PV system power generation, primarily due to unpredictable weather conditions. Additionally, PV systems face continuous exposure to various faults and anomalies that can impact their productivity and profitability. This Reprint centers on artificial intelligence (AI)-driven approaches for photovoltaic energy forecasting, modeling, and monitoring. The importance of AI methods in predicting, modeling, and detecting faults in PV systems is crucial in today's energy landscape. AI has emerged as a transformative force, addressing inherent challenges associated with solar energy production. The studies within this Reprint include empirical research across various subjects, encompassing machine learning and IoT for PV monitoring. The Reprint explores the effects of shading and dust on PV systems and presents AI-driven solutions. It also delves into PV modeling, optimization, and innovative strategies to enhance accuracy. In summary, this Reprint offers a concise yet comprehensive exploration of AI applications in solar energy, catering to researchers, practitioners, and educators in the field. |
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| AbstractList | Solar photovoltaic (PV) systems are pivotal and transformative technologies at the forefront of the global shift toward sustainable energy solutions. The primary challenge in solar energy production lies in the volatility and intermittency of PV system power generation, primarily due to unpredictable weather conditions. Additionally, PV systems face continuous exposure to various faults and anomalies that can impact their productivity and profitability. This Reprint centers on artificial intelligence (AI)-driven approaches for photovoltaic energy forecasting, modeling, and monitoring. The importance of AI methods in predicting, modeling, and detecting faults in PV systems is crucial in today's energy landscape. AI has emerged as a transformative force, addressing inherent challenges associated with solar energy production. The studies within this Reprint include empirical research across various subjects, encompassing machine learning and IoT for PV monitoring. The Reprint explores the effects of shading and dust on PV systems and presents AI-driven solutions. It also delves into PV modeling, optimization, and innovative strategies to enhance accuracy. In summary, this Reprint offers a concise yet comprehensive exploration of AI applications in solar energy, catering to researchers, practitioners, and educators in the field. |
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| Editor | Sun, Ying Taghezouit, Bilal Dairi, Abdelkader Harrou, Fouzi |
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| SubjectTerms | adaptive cuckoo search optimization (ACS) anomaly detection artificial intelligence artificial intelligence (AI) BIPV cloud estimation complex partial shading (CPS) cost minimization dandelion optimizer deep learning deep reinforcement learning double deep Q network dragonfly (DA) dust cleaning electrical faults ensemble bagged trees fault diagnosis gradient boosting algorithms gray wolf optimizer incremental conductance (InC) internet of things local maxima (LM) machine learning maximum power point tracker (MPPT) maximum power point tracking (MPPT) monitoring system n/a optimization parameter estimation partial shading (PS) partial shading conditions (PSCs) particle swarm optimization (PSO) perturb and observe (P&O) photovoltaic photovoltaic (PV) photovoltaic (PV) systems photovoltaic energy photovoltaic energy prediction photovoltaic mathematical model photovoltaic power forecast photovoltaic systems photovoltaics predictive hybrid model PV power forecasting recurrent neural networks renewable energy shading shading ratio estimation shallow neural networks solar energy statistical control charts Technology, Engineering, Agriculture, Industrial processes Technology: general issues Temporal Fusion Transformer total-sky imaging two-diode model |
| Title | Artificial Intelligence Techniques for Solar Irradiance and PV Modeling and Forecasting |
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