An Intelligent Model to Predict Energy Performances of Residential Buildings Based on Deep Neural Networks

Abstract

As the level of greenhouse gas emissions increases, so does the importance of the energy performance of buildings (EPB). One of the main factors to measure EPB is a structure’s heating load (HL) and cooling load (CL). HLs and CLs depend on several variables, such as relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution. This research uses deep neural networks (DNNs) to forecast HLs and CLs for a variety of structures. The DNNs explored in this research include multi-layer perceptron (MLP) networks, and each of the models in this research was developed through extensive testing with a myriad number of layers, process elements, and other data preprocessing techniques. As a result, a DNN is shown to be an improvement for modeling HLs and CLs compared to traditional artificial neural network (ANN) models. In order to extract knowledge from a trained model, a post-processing technique, called sensitivity analysis (SA), was applied to the model that performed the best with respect to the selected goodness-of-fit metric on an independent set of testing data. There are two forms of SA—local and global methods—but both have the same purpose in terms of determining the significance of independent variables within a model. Local SA assumes inputs are independent of each other, while global SA does not. To further the contribution of the research presented within this article, the results of a global SA, called state-based sensitivity analysis (SBSA), are compared to the results obtained from a traditional local technique, called sensitivity analysis about the mean (SAAM). The results of the research demonstrate an improvement over existing conclusions found in literature, which is of particular interest to decision-makers and designers of building structures.

Method for Building Information Modeling Supported Project Control of Nearly Zero-Energy Building Delivery

“…This paper proposes an NZEB delivery control method based on controlling the parameters that impact the energy need for heating and cooling because they are the basis of calculation of right sizing HAVC systems. The energy needs for heating and cooling are mostly analyzed factors of whole building energy needs (e.g., [13,[30][31][32][33][34]) because they are recognized as the main characteristic of building envelope in protecting the building environment from the outside climatic conditions [29].…”

Forecasting of Energy Demands for Smart Home Applications

“…Similarly, Sadeghi et al [28] utilized DNN for predicting the energy performance in residential buildings. To extract knowledge from a trained model, a postprocessing technique called sensitivity analysis (SA) was applied for performing the best with the reference to goodness-of-fit metric on an independent set of testing data.…”

Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics

“…Sadeghi et al [23] applied DNNs, with Multi-Layer Perceptron (MLP) network, to predict heating load and cooling load for a variety of structures. With various extensive testing of data pre-processing techniques, DNNs enhanced previous ANN models.…”