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Model Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air...

Predicting the thermal comfort of operators in enclosed cabins under extreme operational conditions is crucial for the enhanced and optimal design of cabin air circulation systems. In this study, an improved supervised machine learning algorithm, namely a Grey Principal Component Analysis (G-PCA) was proposed to evaluate the operators’ thermal comfort. The comprehensive dataset was first attained and constructed from the proposed 32 indicators, which recorded each tested object’s EEG and...

In Hong Kong, 45 % of the population live in public housing buildings. This study aimed to develop deep neural network (DNN) models for efficiently predicting the ventilation rates in public housing buildings in Hong Kong in the era of modular flat design. First, a database of ventilation rates in Cheung Tai House, a representative public housing building, under 23 wind conditions and 32 different ventilation settings, was obtained by means of computational fluid dynamic (CFD) and multi-zone...

To meet the thermal comfort requirements of room occupants, a fast and accurate method for predicting indoor high-resolution 3D airflow distribution is necessary, which can be combined with heating, ventilation, and air conditioning (HVAC) systems to adjust the indoor environment. Artificial neural networks (ANN) can establish complex mappings between variables with nonlinear relationships. The aim of this study was to verify the feasibility of an ANN for the fast and accurate prediction of...

The aim of this study is to present a novel data-driven approach developed for space heating energy demand calculation of the whole EU building stock. To develop a computationally efficient bottom-up model that takes into account building physics parameters and details of the building stock make-up, an artificial neural network (ANN) is trained on a dataset of precise building-physics models. For this purpose, 2025 building energy simulations were performed in this study, ensuring...

With increasing energy consumption, how to achieve the energy-saving operation of air-conditioning systems is crucial for improving the energy efficiency of buildings. The accurate and reliable energy consumption prediction of air-conditioning systems can be useful for optimizing the energy supply and equipment operation strategies. However, most existing studies focus on the prediction of the long-term energy consumption of air-conditioning systems, which usually exceeds the individual...

Deep learning models have been increasingly applied in the field of solar radiation prediction. However, the characteristics of a deep learning black box model restrict its application in practical scenarios such as model predictive control. Because energy system controllers may be unable to make final decisions based solely on the predictions of a black-box model. This study considers both the temporal and spatial dependencies of solar radiation predictions through unfolding sequences and...

Individuals globally spend about 88–92% of their entire time in the indoor environment. The implementation of regularly scheduled systems operation is common in many commercial and residential building types. Occupant Behavior (OB) is highly stochastic, making it difficult to depict the human factor using simple schedules. In the present work, window-opening tendencies were found to be highest in summer and transition seasons and lowest during winter. The Air Changes per Hour (ACH) value...

Solar shading devices, such as Venetian blinds, are effective in controlling heat and light gain in buildings. This study focuses on developing an Artificial Neural Network (ANN) to automate the management of Venetian blinds in order to find a balance between energy savings and visual comfort. Typically, automatic control strategies rely on cut-off angles or maintaining appropriate indoor illuminance. However, finding the optimal trade-off between solar gain and daylight is challenging,...

In response to climate change, urban blue-green infrastructure (UBGI) improves the microclimate of the built environment. In previous research, UBGI (consisting of water and greenery) is found to be a cold source in the summer, alleviating urban thermal stress. However, studies on the heating effect of UBGI in the winter are limited. This effect can improve environmental temperature, which is beneficial to human thermal comfort in the cold season. Therefore, this study conducted the...

In response to climate change, urban blue-green infrastructure (UBGI) improves the microclimate of the built environment. In previous research, UBGI (consisting of water and greenery) is found to be a cold source in the summer, alleviating urban thermal stress. However, studies on the heating effect of UBGI in the winter are limited. This effect can improve environmental temperature, which is beneficial to human thermal comfort in the cold season. Therefore, this study conducted the...

Model-based optimal control has proven its effectiveness in optimizing the performance of central air-conditioning systems in terms of thermal comfort and energy efficiency. It was often assumed that temperature distribution in the entire air-conditioned space is uniform and can be represented by a single or averaged measurement in optimization. However, actual distribution in the air-conditioned space is usually uneven, which can affect thermal comfort and indoor air quality. The dynamics...

Energy studies of buildings are becoming more widespread as stakeholders strive to improve energy efficiency and reduce carbon emissions. As a result, there is an increased need for novel numerical techniques to automatically calibrate building energy models (BEMs) for these energy studies. In this paper, a new automated building calibration methodology was developed, which uses a surrogate (i.e., meta) multilayer perceptron artificial neural network (ANN) to infer unknown building...

This paper investigates the use of dilated causal convolutional neural networks for fine-grained temporal forecasting of building zone states. Specifically, we build and evaluate models using a small set of exogenous features (e.g., external temperature) to autoregressively predict zone airflow setpoints every minute for a 24-h prediction window. We carefully explore the trade-off between generality and specificity in these models, training and evaluating them based on zone, zone type,...

Environmental degradation and carbon emissions have become a major global concern. This has forced policymakers to consider strategic and long-term contingencies to increase carbon sequestration capacity and mitigate the effects of climate change. Soil organic carbon (SOC) provides a reliable long-lasting mechanism to ameliorate climate change and regulate carbon fluxes. However, unanticipated rates of climate change coupled with the dynamic nature of land-use transformation threatens...

Environmental degradation and carbon emissions have become a major global concern. This has forced policymakers to consider strategic and long-term contingencies to increase carbon sequestration capacity and mitigate the effects of climate change. Soil organic carbon (SOC) provides a reliable long-lasting mechanism to ameliorate climate change and regulate carbon fluxes. However, unanticipated rates of climate change coupled with the dynamic nature of land-use transformation threatens...

We present an approach to generate location-specific forecasts of indoor temperature (Ti) and thermal comfort and issue indoor heat warnings for occupational settings. Indoor forecasts are generated using standard outdoor weather forecasting products and an artificial neural network (ANN) trained on-site using local indoor measurements from a low-cost sensor system measuring Ti and indoor physiologically equivalent temperature (PETi). The outcomes are hourly indoor Ti and PET i forecasts....

Energy Poverty (EP) is a widespread problem in Europe. EP detection is hampered by a lack of data and global metrics. Recently, innovative approaches using Artificial Intelligent (AI) techniques have been increasingly applied for the EP alleviation. In this work, studies focused on the application of AI on EP were studied. It was identified that there is not a high number of works that apply AI to fight against EP (considering this problem as a multidimensional phenomenon). Artificial Neural...

Building Energy prediction has emerged as an active research area due to its potential in improving energy efficiency in building energy management systems. Essentially, building energy prediction belongs to the time series forecasting or regression problem, and data-driven methods have drawn more attention recently due to their powerful ability to model complex relationships without expert knowledge. Among those methods, artificial neural networks (ANNs) have proven to be one of the most...