This study aims to develop accurate prediction models for heating and cooling demands in buildings equipped with solar panels. By integrating renewable energy technologies, the goal is to design nearly energy-neutral buildings that significantly reduce energy consumption and enhance overall efficiency.
The research utilizes deep learning models to address variables in building design, an area that previous studies have not fully explored. A dataset from arid climate regions was used to train and test two deep learning models to predict building energy consumption and solar energy output. The evaluation focused on how well the models predicted heating and cooling needs, as well as the amount of energy solar panels would need to generate in order to meet these demands. This approach represents an advancement over previous methodologies by integrating deep learning techniques with energy prediction in the context of arid climates, where energy efficiency is a critical concern.
The deep learning models developed in this study were highly accurate in predicting both heating and cooling requirements and the energy output from solar panels. This suggests that these models can effectively support the design of energy-efficient buildings, ensuring that the solar panels provide enough energy to cover the building’s needs.
This study introduces a novel method for predicting solar panel performance by integrating building characteristics with energy consumption, moving beyond traditional reliance on environmental factors. It optimizes energy management systems for arid climates, enhancing accuracy and applicability. The use of deep learning models with optimization techniques ensures precise and flexible predictions, providing a holistic solution for energy-efficient building design. The findings provide useful insights for architects and builders looking to create nearly zero-energy buildings, advancing the field of green building technologies.
