Artificial Intelligence Techniques for Modeling Indoor Building Temperature under Tropical Climate Using Outdoor Environmental Monitoring

Tzuc, O. May; Livas-García, A.; Torres, M. Jiménez; May, E. Cruz; López-Manrique, Luis M.; Bassam, A.

doi:10.1061/(asce)ey.1943-7897.0000649

Cited by 21 publications

(5 citation statements)

References 45 publications

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“…The performance of the models was MSE < 1 and r > 0.9. Tzuc et al [48] obtained a similar high performance for indoor building temperature models created using MLP, a radial basis function neural network (RBF), and the group method of data handling (GMDH). The authors reported that r = 0.931 and MSE = 1.033 for training and r = 0.929 and MSE = 1.321 for the testing subset.…”

Section: Validation Modellingmentioning

confidence: 93%

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

et al. 2021

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An accurate air-temperature prediction can provide the energy consumption and system load in advance, both of which are crucial in HVAC (heating, ventilation, air conditioning) system operation optimisation as a way of reducing energy losses, operating costs, as well as pollution and dust emissions while maintaining residents’ thermal comfort. This article presents the results of an outdoor air-temperature time-series prediction for a multifamily building with the use of artificial neural networks during the heating period (October–May). The aim of the research was to analyse in detail the created neural models with a view to select the best combination of predictors and the optimal number of neurons in a hidden layer. To meet that task, the Akaike information criterion was used. The most accurate results were obtained by MLP 3-3-1 (r = 0.986, AIC = 1300.098, SSE = 4467.109), with the ambient-air-temperature time series observed 1, 2, and 24 h before the prognostic temperature as predictors. The AIC proved to be a useful method for the optimum model selection in a machine-learning modelling. What is more, neural network models provide the most accurate prediction, when compared with LR and SVR. Additionally, the obtained temperature predictions were used in HVAC applications: entering-water temperature and indoor temperature modelling.

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Section: Validation Modellingmentioning

confidence: 93%

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

et al. 2021

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“…MLP has an input layer, an output layer, and a hidden layer in which each neuron is connected to the mentioned layers. This architecture has been used as a powerful method to predict the indoor temperature and energy consumption of buildings [22,45,51] and assess the occupants' thermal comfort [52,53]. This research started with an MLP model with one hidden layer and four neurons.…”

Section: Methodsmentioning

confidence: 99%

Use of Machine Learning Methods for Indoor Temperature Forecasting

et al. 2021

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Improving the energy efficiency of the building sector has become an increasing concern in the world, given the alarming reports of greenhouse gas emissions. The management of building energy systems is considered an essential means for achieving this goal. Predicting indoor temperature constitutes a critical task for the management strategies of these systems. Several approaches have been developed for predicting indoor temperature. Determining the most effective has thus become a necessity. This paper contributes to this objective by comparing the ability of seven machine learning algorithms (ML) and the thermal gray box model to predict the indoor temperature of a closed room. The comparison was conducted on a set of data recorded in a room of the Laboratory of Civil Engineering and geo-Environment (LGCgE) at Lille University. The results showed that the best prediction was obtained with the artificial neural network (ANN) and extra trees regressor (ET) methods, which outperformed the thermal gray box model.

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“…The goal was for the control agent to balance IAQ and thermal comfort with energy savings in the classroom. Tzuc et al [59] comparatively applied three AI techniques, multilayer perceptron (MLP) and radial basis function (RBF), and a group method of data handling to model and predict the temperature in a building in a tropical climate. They reported that the MLP technique delivered the highest accuracy in terms of the estimation.…”

Section: For Thermal Comfort and Iaq Controlmentioning

confidence: 99%

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Ogundiran,

Asadi,

Gameiro da Silva

2024

Sustainability

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Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

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Artificial Intelligence Techniques for Modeling Indoor Building Temperature under Tropical Climate Using Outdoor Environmental Monitoring

Cited by 21 publications

References 45 publications

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

Neural Approach in Short-Term Outdoor Temperature Prediction for Application in HVAC Systems

Use of Machine Learning Methods for Indoor Temperature Forecasting

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

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