{"id":8539,"date":"2023-07-21T09:11:08","date_gmt":"2023-07-21T09:11:08","guid":{"rendered":"https:\/\/komorebi.ai\/?page_id=8539"},"modified":"2024-01-29T08:26:55","modified_gmt":"2024-01-29T08:26:55","slug":"machine-learning-pv-energy-satellite-data","status":"publish","type":"page","link":"https:\/\/komorebi.ai\/es\/machine-learning-pv-energy-satellite-data\/","title":{"rendered":"Machine Learning nowcasting of PV Energy Using Satellite Data"},"content":{"rendered":"\t\t
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Machine Learning nowcasting of PV Energy Using Satellite Data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Neural Processing Letters, 2019<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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This study focuses on photovoltaic (PV) energy production prediction in peninsular Spain using satellite-measured radiances and clear sky irradiance estimates. Four different machine learning models were applied – two linear (Lasso and linear Support Vector Regression (SVR)) and two non-linear (Deep Neural Networks, specifically Multilayer Perceptrons (MLPs), and Gaussian SVRs). The performance of these models was measured against a clear sky-based persistence model across prediction horizons up to six hours.

The Gaussian SVR outperformed other models with its errors growing slowly over time (average errors of 1.92% for the first three hours, and 2.89% for the last three). MLPs performed comparably at longer horizons (average 3.1% error) but fell short at initial ones (average 2.26% error), though still performed significantly better than the linear models. Linear models, while less accurate, provided insights into specific areas with more significant influence on PV energy predictions by leveraging the spatial sparsity offered by Lasso.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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Deep Learning models to improve the predictability of wind and solar energy, thereby contributing to global sustainable energy solutions and climate change mitigation.<\/p><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Where does it apply?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The predictive technology utilizing machine learning models for photovoltaic (PV) energy production<\/strong> can be applied across various sectors. These include renewable energy companies for optimizing energy production and distribution<\/strong>, government agencies for informed policy development, and research institutions for further exploration of methodologies and findings.<\/p>

Moreover, they can aid weather forecasting centers in enhancing solar irradiance prediction and assist financial sectors in managing risk and formulating investment strategies in energy markets. Lastly, these models can support the refinement of smart grid management and power control strategies.<\/strong><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Why does it matters?<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The reliance on photovoltaic (PV) energy, a form of renewable energy<\/strong>, is increasing worldwide. The integration of machine learning into its prediction processes can significantly impact the accuracy of PV energy production forecasts, contributing to more efficient energy management and grid reliability. The study findings are especially relevant as they highlight the Gaussian SVR model’s effectiveness in forecasting, which can bring improvements in energy planning.<\/strong><\/p>

Additionally, the spatial insights from linear models can help identify the important factors and areas influencing PV energy production, leading to informed decision-making about where future PV installations can be most beneficial. This is crucial in countries like Spain, where renewable energy sources are a growing part of their energy mix.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Machine Learning nowcasting of PV Energy Using Satellite Data<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Neural Processing Letters, 2019<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tAlberto Torres<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tCTO<\/a>\n\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Otras publicaciones<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Ensemble<\/i> de \u00c1rboles de Regresi\u00f3n para la Predicci\u00f3n de Energ\u00eda E\u00f3lica y Radiaci\u00f3n Solar<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Aprendizaje por Refuerzo bajo Amenazas<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Muestreo con Inferencia Variacional a trav\u00e9s de un L\u00edmite Mejorado\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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El estudio destaca c\u00f3mo los modelos de <\/span>Ensemble<\/span><\/i> mejoran las previsiones de energ\u00eda; promoviendo una mejor gesti\u00f3n energ\u00e9tica y estrategias de inversi\u00f3n en el sector de las energ\u00edas renovables.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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El estudio introduce una nueva t\u00e9cnica, los Procesos de Decisi\u00f3n de Markov bajo Amenazas, que permite mejorar la toma de decisiones en escenarios de aprendizaje por refuerzo adversario. El trabajo tiene aplicaciones en ciberseguridad, finanzas e IA.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Una nueva t\u00e9cnica que mejora la inferencia bayesiana en modelos probabil\u00edsticos, aumentando la eficiencia de los procesos de toma de decisiones en diversos campos. Por ejemplo, el an\u00e1lisis financiero, la gesti\u00f3n de riesgos, el aprendizaje autom\u00e1tico y la log\u00edstica.<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRenewable energy, Predictive Analytics<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tReinforcement Learning, Decision Making<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tFinancial and risk analysis, Bayesian inference<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t
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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t

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Saber m\u00e1s >><\/a><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

Machine Learning nowcasting of PV Energy Using Satellite Data Neural Processing Letters, 2019 Abstract This study focuses on photovoltaic (PV) energy production prediction in peninsular Spain using satellite-measured radiances and clear sky irradiance estimates. Four different machine learning models were applied – two linear (Lasso and linear Support Vector Regression (SVR)) and two non-linear (Deep […]<\/p>\n","protected":false},"author":1,"featured_media":8503,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"yoast_head":"\nMachine Learning nowcasting of PV Energy Using Satellite Data - Komorebi AI<\/title>\n<meta name=\"description\" content=\"Machine learning improves photovoltaic energy production prediction in Spain, benefiting sectors like renewable energy and finance.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/komorebi.ai\/es\/machine-learning-pv-energy-satellite-data\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning nowcasting of PV Energy Using Satellite Data - 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