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.
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?
Improved prediction models for renewable energy have vast applications across multiple sectors. These include aiding utility companies in energy allocation and grid management, assisting renewable energy developers with site selection for wind and solar farms, and enhancing meteorological agencies' service quality. Further, they aid policymakers in decision-making processes, enrich research institutions' studies on weather patterns and renewable energy impacts, and support investors with risk assessment and return on investment evaluations for renewable energy infrastructure.
Overall, these models significantly contribute to climate change mitigationby enhancing renewable energy sources' efficiency and reliability.
Overall, these models significantly contribute to climate change mitigationby enhancing renewable energy sources' efficiency and reliability.
Why does it matter?
The significance of this study lies in its potential to enhance the predictability of renewable energy resources. By employing DL models and ensembles, more accurate predictions of wind and solar energy can be attained. Greater accuracy in these forecasts can improve the utility and management of these renewable energy sources, making them a more reliable substitute to traditional energy sources.
This scenario is crucial in the global drive towards sustainable energy solutions and in mitigating the adverse impacts of conventional energy sources on the environment.
This scenario is crucial in the global drive towards sustainable energy solutions and in mitigating the adverse impacts of conventional energy sources on the environment.
Deep Neural Networks for Wind and Solar Energy Prediction
Neural Processing Letters, 2017
Other publications
Machine Learning Nowcasting Of PV Energy Using Satellite Data
Pomeron Physics at the LHC
Poor self‐reported sleep is associated with risk factors for cardiovascular disease
Machine learning models to improve photovoltaic (PV) energy production prediction in Spain benefiting sectors like renewable energy and finance.
The study uses Regge Theory and Pomeron exchange to enhance understanding of hadron collisions. In the long term, it influences advancements in areas like quantum computing, medical technology, defense, and energy.
The study indicates a link between poor sleep and increased cardiovascular disease risk. Emphasizing the need for sleep improvements in health strategies and policies.