Prediction of building's temperature using neural networks models

Ruano, A. E.; Crispim, E. M.; Conceição, Eusébio; Lúcio, Ma Manuela

doi:10.1016/j.enbuild.2005.09.007

Cited by 178 publications

(80 citation statements)

References 18 publications

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“…Another interested work [10] developed an ANN that is able to optimize the thermal properties of external walls, in order to improve the thermal efficiency of dwellings; specifically, the thermal conductivity and the volumetric specific heat were evaluated and optimized. Other works implemented ANN for a similar goal: in order to predict the building's temperature [12], for improving the thermal conditions in residential buildings [13], or for improving both energy consumption and thermal comfort sensation [11].…”

Section: Introductionmentioning

confidence: 99%

“…Several studies were carried out for predicting the thermal behavior of buildings envelope [7][8][9][10][11][12][13] and for evaluating the energy demand [14][15][16][17][18][19][20][21][22][23] by using Artificial Neural Network (ANN). Pandey et al [7] evaluated the indoor temperature using two prototype rooms (1m × 1m × 1m) in which different cooling techniques were built and tested; specifically ANN was developed using different training functions and considering the external climate conditions (outdoor temperature, wind speed, and solar intensity).…”

Section: Introductionmentioning

confidence: 99%

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Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions

et al. 2014

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Many studies in Italy showed that buildings are responsible for about 40% of total energy consumption, due to worsening performance of building envelope; in fact, a great number of Italian buildings were built before the 1970s and 80s. In particular, the energy consumptions for cooling are considerably increased with respect to the ones for heating. In order to reduce the cooling energy demand, ensuring indoor thermal comfort, a careful study on building envelope performance is necessary. Different dynamic software could be used in order to evaluate and to improve the building envelope during the cooling period, but much time and an accurate validation of the model are required. However, when a wide experimental data is available, the Artificial Neural Network (ANN) can be an alternative, simple and fast tool to use. In the present study, the indoor thermal conditions in many dwellings built in Umbria Region were investigated in order to evaluate the envelope performance. They were recently built and have very low energy consumptions. Based on the experimental data, a feed forward network was trained, in order to evaluate the different envelopes performance. As input parameters the outdoor climatic conditions and the thermal characteristics of building envelopes were set, while, as a target parameter, the indoor air temperature was provided. A good training of network was obtained with a high regression value (0.9625) and a very small error (0.007 °C) on air temperature. The network was also used to simulate the envelope behavior with new innovative glazing systems, in order to evaluate and to improve the energy performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions

et al. 2014

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“…Models based on physical principles can vary largely from extremely complex to more simplified structures with respect to the number of parameters and variables [14][15][16][17] but they usually need detailed information on the building characteristics and are in general too computationally heavy to be effectively used for control purposes. On the other hand, data driven models [18][19][20][21][22][23][24][25][26][27][28] are based solely on measurements and are typically identified without information on the physical nature of the building properties. Hybrid or grey box models are a combination of data driven and physical modelling approaches [29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Model for Indoor Temperature Prediction in Buildings

2018

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A novel dynamic model for the temperature inside buildings is presented, aiming to improve energy efficiency by providing predictive information on the heat demand. To analyse the performance and generalizability of the modelling approach, real measurement data was gathered from five different types of buildings. Easily available data from various sources was utilized. The chosen model structure leads to a minimal number of input variables and free parameters. Simulations with real data from five buildings, and applying the identical model structure showed that the average modelling error during the 28-h prediction horizon was constantly below 5%. The results thus demonstrate that the model structure can be standardized and easily applied to predict the indoor temperatures of large buildings. This would finally enable demand side management and the predictive optimization of the heat demand at city level.

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“…Through this predictability and adaptability, the ANN-based control logic can provide a more comfortable and stable thermal environment. Its superiority over mathematical methods, such as regression models or proportional-integral-derivative (PID) controllers has been proven based on aspects of thermal comfort and energy efficiency [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes

2014

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This study aims at developing an indoor temperature control method that could provide comfortable thermal conditions by integrating heating system control and the opening conditions of building envelopes. Artificial neural network (ANN)-based temperature control logic was developed for the control of heating systems and openings at the building envelopes in a predictive and adaptive manner. Numerical comparative performance tests for the ANN-based temperature control logic and conventional non-ANN-based counterpart were conducted for single skin enveloped and double skin enveloped buildings after the simulation program was validated by comparing the simulation and the field measurement results. Analysis results revealed that the ANN-based control logic improved the indoor temperature environment with an increased comfortable temperature period and decreased overshoot and undershoot of temperatures outside of the operating range. The proposed logic did not show significant superiority in energy efficiency over the conventional logic. The ANN-based temperature control logic was able to maintain the indoor temperature more comfortably and with more stability within the operating range due to the predictive and adaptive features of ANN models. OPEN ACCESSEnergies 2014, 7 7246

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Prediction of building's temperature using neural networks models

Cited by 178 publications

References 18 publications

Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions

Building Behavior Simulation by Means of Artificial Neural Network in Summer Conditions

A Dynamic Model for Indoor Temperature Prediction in Buildings

Evaluation of Artificial Neural Network-Based Temperature Control for Optimum Operation of Building Envelopes

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