Summary of Predicting Solar Heat Production to Optimize Renewable Energy Usage, by Tatiana Boura and Natalia Koliou and George Meramveliotakis and Stasinos Konstantopoulos and George Kosmadakis
Predicting Solar Heat Production to Optimize Renewable Energy Usage
by Tatiana Boura, Natalia Koliou, George Meramveliotakis, Stasinos Konstantopoulos, George Kosmadakis
First submitted to arxiv on: 16 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Systems and Control (eess.SY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach for optimizing solar energy-based space heating and domestic hot water systems combines machine learning models with solar irradiance predictions to ensure user demand is fully met throughout the year. The study focuses on developing a predictive model that accurately forecasts solar thermal production, allowing for optimal control of auxiliary heating systems like boilers and heat pumps. The proposed method integrates advanced machine learning techniques with real-world data from various weather stations, enabling accurate forecasting and efficient energy management. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers is working on making homes more energy-efficient by using the sun’s power to heat water and provide warmth. Right now, they’re trying to figure out how to predict when the sun will be strong enough to meet a household’s heating needs, so they can use backup systems like boilers only when needed. The goal is to make sure people have enough hot water and warmth all year round while reducing their environmental impact. |
Keywords
» Artificial intelligence » Machine learning
