A forecasting strategy based on wireless sensing for thermal comfort optimization in smart buildings

Abstract

The prediction of thermal comfort in smart buildings is addressed in this article to support the energy manager in the management of the heating, ventilation, and air conditioning systems. An efficient control is fundamental to meet the user satisfaction as well as the energy cost reduction. The proposed method based on a customized support vector regression technique predicts and suggests to the energy managers the indoor target temperature required to obtain the desired comfort throughout the building. A network of low‐cost wireless sensors has been deployed to measure the indoor conditions of a real indoor scenario. The prediction performance has been experimentally validated and the obtained results have pointed out a temperature prediction error lower than 1°C and the achievement of the thermal comfort requirements.

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

“…Most of the scientific articles summarized in Table S11, presented in the Supplementary Materials file, analyze smart buildings in general (75%), while 12.5% consider smart homes, and the remaining 12.5% consists of studies regarding commercial buildings. The authors of these scientific articles make use of different types of sensors in their analyses, including wireless sensor networks [23,41,51,94]; thermal sensors [2]; passive infrared motion detecting sensors [97]; temperature and humidity sensors [41]; occupancy and light sensors [13]; and energy smart meters, building management systems, and weather stations [44].…”

“…The reasons for implementing the Support Vector Regression (SVR) integrated with sensor networks in smart buildings were mainly related to forecasting electricity consumption [13,23,41,44]; controlling smart lighting [94]; human behavior recognition [2]; thermal comfort optimization [51]; and short-term prediction of occupancy [97].…”

“…Regarding the devised research methods, in [ The authors of the scientific papers using the Support Vector Regression (SVR) method integrated with sensor devices in smart buildings chose various performance metrics, including Coefficient of Determination (R 2 ), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) [23,41]; Average Error and Error Rate [2]; Prediction Error [51]; Accuracy [97]; comparison between the actual energy consumption per day and predicted energy consumption per day [13]; Coefficient of Variation (CV) and Standard Error [44]; and Root Mean Square Error (RMSE) along with Normalized Mean Square Error (NMSE) [94].…”

“…With respect to the five most recent scientific articles addressing the Support Vector Regression (SVR) method integrated with sensor devices in smart buildings (Table 11) it can be observed that paper [23] was previously reviewed when analyzing the most recent scientific articles that integrate Neural Networks for classification purposes with sensor devices in smart buildings (Table 5); paper [2] was reviewed previously when analyzing the most recent scientific articles that integrate Support Vector Machines with sensor devices in smart buildings (Table 1); article [41] was reviewed previously when analyzing the most recent scientific articles that integrate Linear Regression with sensor devices in smart buildings (Table 9). In [51], Viani et al addressed issues regarding the thermal comfort forecasting in smart buildings in order to improve the management of the Heating, Ventilation, Air Conditioning (HVAC) systems, to fulfill the users' requirements and to obtain reduced energy costs. Using a Wireless Sensor Network in order to evaluate the indoor conditions, the authors developed a customized SVR technique in order to determine the indoor temperature necessary to ensure the comfort of the inhabitants.…”

Research on Deep Learning Energy Consumption Prediction Based on Generating Confrontation Network

“…In the prediction of building energy consumption, multiple linear regression, genetic optimization algorithms and artificial neural networks are often used to process or train energy consumption data to achieve prediction [11]- [13]. Artificial neural network algorithm has been widely recognized and applied in the HVAC industry as a new research tool [14]. Moreover, in the prediction of energy consumption of public buildings, experts and scholars have not only used one method, but also used a combination of methods to predict energy consumption, and achieved excellent results [15], [16].…”