Deep learning–based urban energy forecasting model for residential building energy efficiency

Abstract

Sustainable and inventive city design is becoming more and more dependent on the use of cutting-edge technology as smart cities develop further. Energy efficiency optimization in residential structures is an essential part of the puzzle as it helps conserve resources and keeps the planet habitable. An enhanced Deep Neural Network (DNN) model for household energy efficiency predictions is presented in this research. Our model uses a large dataset of building features, weather, occupancy patterns and energy usage histories. Data is preprocessed, features are engineered and hyperparameters are tweaked to improve DNN prediction. Scalable, easy-to-understand models are essential, as are shifting urban areas and energy landscapes. In this work, the authors have evaluated the proposed model with basic model with different optimizers. Initially, the Stochastic Gradient Descent optimizer applied that gained 91.02% Recall, 93.47% Precision, 93.28% F1-Score, 0.0153 MSE, 0.0166 RMSE and 0.0165 MAE. The proposed model gained 99.52% Recall, 98.91% Precision, 99.09% F1-Score, 0.0140 MSE, 0.0137 RMSE and 0.0139 MAE. By monitoring, analyzing and making decisions in real time, smart city systems can help planners understand energy usage trends. The optimized DNN model advances smart city development by promoting sustainability and resource optimization. Predicting residential buildings’ energy efficiency provides proactive energy savings, cost reduction and environmental impact mitigation. The suggested DNN model shows how smart cities use cutting-edge urban planning to become more sustainable, efficient and resilient.

Multi-Objective Plum Tree Algorithm and Machine Learning for Heating and Cooling Load Prediction

“…Even though they are not directly comparable to our results because of different preprocessing configurations or cross-validation settings, the recent studies based on the latest deep learning methods returned promising results. For example, the approach presented in [51] based on deep neural networks returned a root mean square error equal to 0.0137. However, compared to our approach, the problem was converted into an image processing problem by transforming the data into image datasets.…”