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Neural network, ARX, and extreme learning machine models for the short-term prediction of temperature in buildings
2019
Building Simulation
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), and 0.065°C for the neural network model (in the case of available future weather data). ...
Autoregressive models with exogenous inputs (ARX), neural network models (NN), and extreme learning machine models (ELM) are considered. ...
+aMq -M , and B(q) = b1+b2q -1 +...+bMq -M+1 . The linear ARX model can be used to construct a predictive model for the thermal response of a building. ...
doi:10.1007/s12273-019-0548-y
fatcat:joppjhiorjff7hqj4dwrbewrtm
Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey
2011
Indoor and Built Environment
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In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor ...
In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. ...
[14] evaluated the usage of neural network models for estimation of indoor air temperature. ...
doi:10.1177/1420326x11422163
fatcat:iwlwrudthbbjvjsnjwy6ejgsdu
Heat demand model for district heating simulation
2018
MATEC Web of Conferences
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It is assumed that the heat demand is the most challenging part of the prediction, and therefore the accuracy and quality of these models will be the most significant to the accuracy of the entire future ...
The fundamental idea is to build a modular model for specific district heating and start from the endpoints -from the individual consumption objects that will be interconnected through the distribution ...
Many researchers use neural networks for modelling the nonlinearity of heat demand temperature dependence. ...
doi:10.1051/matecconf/201821002044
fatcat:f4t5cxzhpjalfop2wb4p462gci
Learning and training techniques in fuzzy control for energy efficiency in buildings
2011
Logic Journal of the IGPL
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Finally, a supervised dynamic neural network model and identification techniques are applied to FC learning and training. ...
Firstly, the dynamic thermal performance of different variables is mathematically modelled for each specific building type and climate zone. ...
Finally, different techniques were applied to obtain a suitable prediction model, which was used for the prediction of indoor temperature. The results show a new promising method for learning FCs. ...
doi:10.1093/jigpal/jzr022
fatcat:m55e23dfjbamjppmn6d7prhx34
Simulation of thermal behavior of residential buildings using fuzzy active learning method
2015
Journal of Fuzzy Set Valued Analysis
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In addition, it is necessary to predict and control indoor temperature for minimization of energy consumption. ...
In this paper, a fuzzy modeling technique called Modified Active Learning Method (MALM) was introduced and utilized for fuzzy simulation of indoor and inner surface temperatures in residential buildings ...
The authors thank Key Laboratory State of Faculty of Architecture (South China University of Technology) for the preparation of inner surface and indoor temperature measurement tools. ...
doi:10.5899/2015/jfsva-00208
fatcat:zjybntc74fhxvgqia5xifv5mmq
Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings?
2019
Energy and Buildings
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Whilst, logistic GAMs were shown to adequately predict the window opening state, incorporating knowledge of the window state did not significantly improve the accuracy of the indoor temperature predictions ...
A novel application combining semi-parametric Generalized Additive Models (GAMs) with logistic GAMs was developed to forecast indoor temperatures and window opening states during prolonged heatwaves. ...
Acknowledgements This research was made possible by the Engineering and Physical Sciences Research Council (EPSRC) support for the 'London-Loughborough (LoLo) Centre for Doctoral Training in Energy Demand ...
doi:10.1016/j.enbuild.2019.03.048
fatcat:rhnhj3d7abevhe7yxxge7tt3mq
Long Term Solar Power Generation Prediction using Adaboost as a Hybrid of Linear and Non-linear Machine Learning Model
2021
International Journal of Advanced Computer Science and Applications
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neural network ARX models applied to a prediction of the indoor
statistical methods. ...
to the Feed-forward Neural Network (FFNN), SVM and
There always been a comparison in linear and non-linear regression model. 72 hour ahead prediction was made into the
models with respect ...
doi:10.14569/ijacsa.2021.0121161
fatcat:arvdoolxhzh6nkwe226vr4pvhi
Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls
2020
Energies
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The implementation of model predictive controls (MPCs) in buildings represents an important opportunity to reduce energy consumption and to apply demand side management strategies. ...
With a maximum deviation of 0.5 °C from the indoor set-point temperature, the physical-based model shows better performance in following the system dynamics, while the value rises to 1.8 °C in presence ...
The most common black box models are [18] support vector machines (SVM) [19] , statistical regression (e.g., linear auto regressive models with exogenous inputs, ARX [20] ), and artificial neural networks ...
doi:10.3390/en13123125
fatcat:hvsiklac3zconex2s5rrdwnopq
Modeling of a Building System and its Parameter Identification
2013
Journal of Electrical Engineering and Technology
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This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. ...
This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building. ...
Moreover, [14] applied an ARX model and a neural network ARX model to the prediction of indoor temperature and relative humidity of an unoccupied residential building. ...
doi:10.5370/jeet.2013.8.5.975
fatcat:xpwus3ldnjgudb7m3phjiu6vdm
Artificial neural networks in energy applications in buildings
2006
International Journal of Low-Carbon Technologies
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Results presented in this paper are testimony to the potential of artificial neural networks as a design tool in many areas of building services engineering. ...
Artificial neural networks (ANNs) are nowadays accepted as an alternative technology offering a way to tackle complex and ill-defined problems. ...
Prediction of the indoor air temperature Mechaqrane and Zouak [24] used a neural network auto regressive with exogenous input (NNARX) model to predict the indoor temperature of a residential building ...
doi:10.1093/ijlct/1.3.201
fatcat:seexwykmtvechnufftapndwvl4
Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression
2014
Energy Conversion and Management
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The prediction method includes a combination of two separate time-indexed ARX models to improve prediction accuracy of the cooling load over different forecasting periods. ...
The numerical case studies show the proposed prediction method performs better than some ANN and ARX forecasting models for the given test data set. ...
Acknowledgement This study was partially supported by the Nebraska Center for Energy Sciences Research and the New Faculty Research Fund of Ajou University. ...
doi:10.1016/j.enconman.2013.12.060
fatcat:ur6gm3ki75gxxm67b4tbhemj6q
Comparison and Simulation of Building Thermal Models for Effective Energy Management
2015
Smart Grid and Renewable Energy
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These lead to use inverse models to describe thermal behavior and to evaluate the energy consumption of buildings. ...
In this paper, we propose a detailed review and simulation of the main thermal building models. ...
Acknowledgements This work was supported by Laboratoire des technologies de l'énergie (LTE) d'Hydro-Québec, Natural Science and Engineering Research Council of Canada and Fondation UQTR. ...
doi:10.4236/sgre.2015.64009
fatcat:awcp52kcv5haxg57trobu3zqtq
Use of Machine Learning Methods for Indoor Temperature Forecasting
2021
Future Internet
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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 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. ...
Wang and Chen [78] compared three data-driven models, a linear black-box model (ARX), a non-linear black-box model (ANN), and a gray box model in predicting the indoor temperature of a single-zone house ...
doi:10.3390/fi13100242
fatcat:ppv3ck46gjgwjoyf7aw3xxwo2e
Improving Energy Efficiency in Buildings Using Machine Intelligence
[chapter]
2009
Lecture Notes in Computer Science
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Finally, neural projections and identification techniques are applied, in order to detect fluctuations in room temperatures and, in consequence, thermal insulation failures. ...
Improving the detection of thermal insulation in buildings -which includes the development of models for heating and ventilation processes and fabric gain -could significantly increase building energy ...
We would like to extend our thanks to Phd. Magnus Nørgaard for his marvellous freeware version of Matlab Neural Network Based System Identification Toolbox. ...
doi:10.1007/978-3-642-04394-9_95
fatcat:qetpeal73fa27cnmlcfj2fimla
WSN BASED THERMAL MODELING: A NEW INDOOR ENERGY EFFICIENT SOLUTION
2015
International Journal on Smart Sensing and Intelligent Systems
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A linear approximation of these models makes it possible to estimate the EITTC of building room. ...
Accordingly, a low cost, energy-efficient, wide-applicable indoor thermal modeling solution is developed by combining Wireless Sensor Network (WSN) and Artificial Neural Network (ANN). ...
Previous studies outlined that ANN model outperformed Auto-Regressive(ARX) models in predicting the indoor temperature because the ANN models are more sensible to the nonlinearities of the thermal effects ...
doi:10.21307/ijssis-2017-787
fatcat:vktsquvwh5gcbcchzbxybecmxa
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