Deep Neural Networks for Wind and Solar Energy Prediction
Neural Processing Letters, 2017
Abstract
Deep Learning models are being used in this study to predict wind and solar energy. The inputs for these predictions are wind and solar radiation forecasts derived from Numerical Weather Prediction systems that have a distinct 2D spatial structure.
The results show noticeable improvements in the predictions of individual Deep Learning (DL) models and DL ensembles over more traditional methods like Support Vector Regression.
Deep Learning models to improve the predictability of wind and solar energy, thereby contributing to global sustainable energy solutions and climate change mitigation.
Where does it apply?
The predictive technology utilizing machine learning models for photovoltaic (PV) energy production can be applied across various sectors.
These include renewable energy companies for optimizing energy production and distribution, government agencies for informed policy development, and research institutions for further exploration of methodologies and findings.
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.
Why does it matters?
The reliance on photovoltaic (PV) energy, a form of renewable energy, 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.
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.
Deep Neural Networks for Wind and Solar Energy Prediction
Neural Processing Letters, 2017
Otras publicaciones
Predicción de Energía Fotovoltaica mediante Aprendizaje Automático utilizando Datos Satelitales
Física del Pomeron en el LHC
El sueño autoinformado deficiente se asocia con factores de riesgo de enfermedades cardiovasculares
Modelos de aprendizaje automático para mejorar la predicción de la producción de energía fotovoltaica en España, beneficiando sectores como la energía renovable y las finanzas.
El estudio utiliza la Teoría de Regge y el intercambio de Pomeron para mejorar la comprensión de las colisiones de hadrones. A largo plazo, influyendo en avances en áreas como la computación cuántica, la tecnología médica, la defensa y la energía.
El estudio indica un vínculo entre el sueño deficiente y un mayor riesgo de enfermedades cardiovasculares, enfatizando la necesidad de mejoras en estrategias del sueño y políticas de salud.