Smart Building: Use of the Artificial Neural Network Approach for Indoor Temperature Forecasting

Abstract

Abstract:The smart building concept aims to use smart technology to reduce energy consumption, as well as to improve comfort conditions and users' satisfaction. It is based on the use of smart sensors and software to follow both outdoor and indoor conditions for the control of comfort, and security devices for the optimization of energy consumption. This paper presents a data-based model for indoor temperature forecasting, which could be used for the optimization of energy device use. The model is based on an artificial neural network (ANN), which is validated on data recorded in an old building. The novelty of this work consists of the methodology proposed for the development of a simplified model for indoor temperature forecasting. This methodology is based on the selection of pertinent input parameters after a relevance analysis of a large set of input parameters, including solar radiation outdoor temperature history, outdoor humidity, indoor facade temperature, and humidity. It shows that an ANN-based model using outdoor and facade temperature sensors provides good forecasting of indoor temperatures. This model can be easily used in the optimal regulation of buildings' energy devices.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…A total of 45.5% of the scientific articles summarized in Table S10, presented in the Supplementary Materials file, analyzed smart buildings in general; the same percentage of papers considered smart homes, while the remaining 9% analyzed both smart homes and smart buildings. The authors of these scientific papers make use of different types of sensors in their analyses, including sensors for registering the electricity consumption [22]; Wireless Sensor Networks (WSNs) [23,45,96]; Passive Infrared (PIR) sensors or motion detectors [75,97]; smart metering systems and sensors installed by the residential consumer, corresponding to 15 individual appliances [95]; weather sensors [12]; flowmeter sensors [43]; temperature sensors, external humidity sensors, solar radiation sensors [98]; thermal sensors [2]; and door/window entry point sensors, electricity power usage sensors, bed/sofa pressure sensors, and flood sensors [75].…”

“…With respect to the reasons for implementing the Neural Networks for regression purposes integrated with sensor devices in smart buildings, these were mainly related to forecasting electricity consumption [12,22,23,45,95]; identifying the occurrence of a specific pattern in a Water Management System (WMS) [43]; indoor temperature monitoring and forecasting [96,98]; human behavior recognition [2,75]; and short-term prediction of occupancy [97].…”

“…With respect to the devised research methods, in [22], The performance metrics considered in the scientific papers using the Neural Networks for Regression Purposes integrated with sensor devices in smart buildings included Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) [12,22,23,45]; Coefficient of Determination (R 2 ) [23]; Mean Absolute Percentage Error (MAPE) [23,45]; Mean Squared Error (MSE) [45,95,98]; Correlation Coefficient (R) [95,98]; the differences between the real consumption and the forecasted ones [95]; Pearson Coefficient [12]; Accuracy [43,97]; Precision, Recall, and F-Measure [43]; Average Error and Error Rate [2]; Mean Absolute Error (MAE) [96]; and Root Mean Squared Error (RMSE) [75].…”

Perceived Importance of Smart and Sustainable Building Features from the Users’ Perspective

“…Smart and sustainable buildings are the ones that through their physical design and ICT installations are responsive and adaptive to the changing environment and needs of users throughout their lifetimes. Because of the adaptive nature of smart and sustainable buildings, the usage of materials, water, and energy resources can be optimized while comfortable and healthy indoor environments can be achieved [2][3][4]. As a building block of smart cities, smart and sustainable buildings promote sustainable urbanization, healthy life style, and social inclusion [5][6][7][8].…”

A Dynamic Model for Indoor Temperature Prediction in Buildings

“…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].…”