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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...

Cooking can generate substantial heat from cooking equipment, potentially resulting in reduced thermal comfort levels if this excess heat is not adequately dissipated. Additionally, it can significantly affect indoor air quality (IAQ), not only in kitchen spaces but also in adjacent areas lacking sufficient ventilation. To ensure a healthy and comfortable indoor environment while avoiding unnecessary energy use, this research proposes an approach for detecting equipment usage. Utilizing deep...

The use of Artificial Intelligence (AI) technologies in buildings can assist in reducing energy consumption through enhanced control, automation, and reliability. This review aims to explore the use of AI to enhance energy efficiency throughout various stages of the building lifecycle, including building design, construction, operation and control, maintenance, and retrofit. The review encompasses multiple studies in the field published between 2018 and 2023. These studies were identified...

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...

Thermal comfort prediction is vital in achieving a good indoor environment and efficient energy management. However, thermal comfort is strongly nonlinear and dynamically changing over time, making it difficult to predict thermal comfort accurately. Two real-time thermal comfort prediction models on multi-time scales are proposed based on deep learning algorithms. The indirect prediction model for thermal comfort integrates the bidirectional long and short-term memory network to predict...

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,...

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,...

Temperature sensors may produce a measurement error of up to 1 °C because of the influence of solar radiation. In order to obtain a relatively minimal temperature error, a new temperature observation system was proposed in this paper for measuring surface air temperatures. Firstly, a radiation shield was designed with two aluminum plates, eight vents, and a multi-layer structure which is able to resist direct solar radiation, reflected radiation, and upwelling long-ware radiation, as well as...

Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air...

Canadian buildings have been primarily designed to withstand cold and long winters, not hot summers. With climate change and the increase in the intensity and severity of heatwaves, it has become important to investigate overheating in buildings. However, there are limited studies on assessing and mitigating the overheating risk in existing buildings that house vulnerable populations in cold climates, especially in Canada. This paper provides a framework for the systematic assessment of...

There is a growing body of literature that recognizes that natural ventilation plays a vital role in indoor air quality, thermal comfort and building energy consumption. This paper systematically reviews the previously published research of the most efficient and typical natural ventilation type - cross ventilation, aiming to present the main research topics in contemporary research and provide an agenda for future studies. The methodologies, airflow pattern, ventilation models and...

Recently, Occupant-Centric Control (OCC) strategies have gained mounting interest. Previous studies made use of OCC strategies for adjusting the operation of heating/cooling systems, improving indoor thermal comfort and governing mechanical ventilation systems. However, a very limited number of studies have applied OCC strategies to natural ventilation systems. Further, the feasibility of establishing OCC strategies for controlling indoor thermal comfort, energy use and specifically air...

The occupants' presence, activities, and behaviour can significantly impact the building's performance and energy efficiency. Currently, heating, ventilation, and airconditioning (HVAC) systems are often run based on assumed occupancy levels and fixed schedules, or manually set by occupants based on their comfort needs. However, the unpredictability and variability of occupancy patterns can lead to over/under the conditioning of space when using such approaches, affecting indoor air quality...