A Hyper Parametrized Deep Learning Model for Analyzing Heating and Cooling Loads in Energy Efficient Buildings

E. M. Abdelkader, N. Elshaboury, Eslam Ali, Ghasan Alfalah, Ahmed Mansour, Abobakr Alsakkaf
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Abstract

The huge increase in energy consumption in recent decades, has made it cumbersome to anticipate energy usage in the residential sector. However, despite substantial advancements in computation and simulation, the modelling of residential building energy use is still in need of improvement for efficient and reliable solutions. To this end, the overarching objective of this research study is to construct a self-adaptive model (HBO-DL) for predicting the amounts of heating and cooling loads in residential buildings. The developed HBO-DL model is envisioned on coupling Bayesian optimization with deep learning neural network. Five statistical metrics of mean absolute percentage error (MAPE), root mean squared error (RMSE), root mean squared logarithmic error (RMSLE), mean absolute error (MAE) and normalized root mean squared error (NRMSE), are leveraged to measure and test the accuracies of the developed HBO-DL. Analytical results explicated that the developed HBO-DL model can endorse informed decision-making and foster energy conservation in built environment.
用于分析节能建筑供热和制冷负荷的超参数化深度学习模型
近几十年来,能源消耗的大幅增长使得住宅领域的能源使用预测变得十分困难。然而,尽管在计算和模拟方面取得了长足的进步,住宅建筑能源使用的建模仍然需要改进,以获得高效可靠的解决方案。为此,本研究的总体目标是构建一个自适应模型(HBO-DL),用于预测住宅建筑的供热和制冷负荷量。所开发的 HBO-DL 模型设想将贝叶斯优化与深度学习神经网络相结合。利用平均绝对百分比误差 (MAPE)、均方根误差 (RMSE)、均方根对数误差 (RMSLE)、平均绝对误差 (MAE) 和归一化均方根误差 (NRMSE) 等五个统计指标来衡量和测试所开发 HBO-DL 的准确性。分析结果表明,所开发的 HBO-DL 模型可以为建筑环境中的知情决策和节能提供支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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