Temperature Prediction in a Public Building Using Artificial Neural Network

Romazanov, Artur; Захаров, А. И.; Zakharova, Irina G.

doi:10.2991/aisr.k.201029.006

Cited by 2 publications

References 11 publications

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Using fuzzy system as AI tool to minimize thermal bridges in design of external walls

Pomada,

Cpałka,

Lacki

et al. 2024

Preprint

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Buildings significantly contribute to climate change, accounting for approximately one-third of global energy consumption and a quarter of CO2 emissions. Therefore, all actions aimed at increasing building energy efficiency are of great importance. This study explores the application of fuzzy system – an artificial intelligence (AI) tool – for optimizing external wall designs, specifically focusing on minimizing thermal bridges at the window-to-wall connection. To achieve this, traditional thermal bridge analysis using the TRISCO program to generate training sets was employed. The data collected from thermal analysis served as input for machine learning. The fuzzy system was then utilized to estimate linear heat transmittance coefficients, which quantify heat loss through thermal bridges. The proposed AI approach demonstrates excellent performance, generating precise linear heat transmittance coefficient values. Importantly, due to its ability to generalize knowledge, the trained system accurately determines the value of the Ψ coefficient for cases not included in the training data – those for which traditional analysis using the TRISCO program had not been previously performed. By leveraging this approach for thermal bridge analysis, it becomes possible to reduce the need for classical analyses, which often involve time-consuming calculations, expensive experiments, and extensive designer expertise in selecting optimal solution.

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Using fuzzy system as AI tool to minimize thermal bridges in design of external walls

Pomada,

Cpałka,

Lacki

et al. 2024

Preprint

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Deep-Learning-Based Strong Ground Motion Signal Prediction in Real Time

AlHamaydeh,

Tellab,

Tariq

2024

Buildings

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Processing ground motion signals at early stages can be advantageous for issuing public warnings, deploying first-responder teams, and other time-sensitive measures. Multiple Deep Learning (DL) models are presented herein, which can predict triaxial ground motion accelerations upon processing the first-arriving 0.5 s of recorded acceleration measurements. Principal Component Analysis (PCA) and the K-means clustering algorithm were utilized to cluster 17,602 accelerograms into 3 clusters using their metadata. The accelerograms were divided into 1 million input–output pairs for training, 100,000 for validation, and 420,000 for testing. Several non-overlapping forecast horizons were explored (1, 10, 50, 100, and 200 points). Various architectures of Artificial Neural Networks (ANNs) were trained and tested, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) networks, and CNN-LSTMs. The utilized training methodology applied different aspects of supervised and unsupervised learning. The LSTM model demonstrated superior performance in terms of short-term prediction. A prediction horizon of 10 timesteps in the future with a Root Mean Squared Error (RMSE) value of 8.43 × 10−6 g was achieved. In other words, the LSTM model exhibited a performance improvement of 95% compared to the baseline benchmark, i.e., ANN. It is worth noting that all the considered models exhibited acceptable real-time performance (0.01 s) when running in testing mode. The CNN model demonstrated the fastest computational performance among all models. It predicts ground accelerations under 0.5 ms on an Intel Core i9-10900X CPU (10 cores). The models allow for the implementation of real-time structural control responses via intelligent seismic protection systems (e.g., magneto-rheological (MR) dampers).

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Temperature Prediction in a Public Building Using Artificial Neural Network

Cited by 2 publications

References 11 publications

Using fuzzy system as AI tool to minimize thermal bridges in design of external walls

Using fuzzy system as AI tool to minimize thermal bridges in design of external walls

Deep-Learning-Based Strong Ground Motion Signal Prediction in Real Time

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