Data mining methods of healthy indoor climate coefficients for comfortable well-being

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Abstract

This article provides information about a currently developed measurement and analysis system ‘Smart Monitoring’, which is used on scientific project in terms of healthy indoor air coefficients, as well as the processing of the collected data for machine learning algorithms. The target is to reduce CO2 emissions caused by wrong ventilation habits in building sector after renovation process in older buildings.

Abstract

This article provides information about a currently developed measurement and analysis system ‘Smart Monitoring’, which is used on scientific project in terms of healthy indoor air coefficients, as well as the processing of the collected data for machine learning algorithms. The target is to reduce CO2 emissions caused by wrong ventilation habits in building sector after renovation process in older buildings.

1 Introduction

The German government has set itself ambitious climate protection targets: By 2020, greenhouse gas emissions have to fall 40% below the level at the state of 1990. Therefore, the building sector is aimed by this project as it is one of the large CO2-producing sectors. The described work is performed with our partners from the renovation management of Bielefeld Sennestadt. The inhabitants live in pre-renovation buildings and they produce more carbon dioxide as normal, whilst losing heating energy caused by poor building state. After renovation and isolation, buildings lose their energy by wrong heating and ventilation habits.

Machine learning algorithms will help to change the heating and ventilation habits of residents by telling them recommendations to get better room climate and to aim lower CO2 emission. We created machine learning algorithms that are basically learned by healthy air quality information according to common DIN standards [DIN EN ISO 7730; ASR… 2012; Detlef et al. 2013; DIN EN 13779] and further extended learned by personal feelings about air quality inside the apartments. Therefore, an algorithm AI has been developed, which is not only able to decide air quality according to German DIN standards AIDIN, which tells that the indoor climate is healthy or not, but also further with AIextended if the occupant feels comfortable or not.

These algorithms could prevent unnecessary CO2 emissions by telling recommendations to residents and influence their heating and ventilation habits in general. Also, they learn a correct and more energy-efficient ventilation and heating behaviour to reduce the CO2 emission after a renovation process.

The Smart Monitoring system provides an interactive user interface inside the apartments. Residents can view information about actual measured data of the air quality in their apartments and also some statistical evaluations. The developed intelligent algorithms provide a prediction of how an occupant is normally feeling by interpreting the actual sensor data. It includes a preliminary processing of measured datasets, their physical relations, statistical analysis, data reduction, targeted learning of German DIN standards and the extended learning of individual indoor air climate feeling. The project is supported by ministry of North Rhine Westphalia (2016–2020) under Nr. 322-FH Struktur.

2 Material and methods of study

The measuring system was developed for the purpose of promoting energy awareness amongst residents and to examine the ageing residential buildings before and after their renovation. It also shows the collected data to occupant’s and provide collecting, managing and transferring Big Data by intelligent algorithms to support scientific work on collected data. Within an interdisciplinary team, a cost-effective measuring system was developed. In addition to expandability, scalability and maintainability, Smart Monitoring also provides secure data communication of measured sensors with strongly cryptographic algorithms.

As seen in Figure 1, the measuring system in apartments consists of a set of sensor computers (Raspberry Pi’s). Each of them collects measurement data of sensors and actuators and sends them by POST request to a RESTful Webservice interface, which is controlled by main server (Figure 1, left side). The main server handles any type of data and saves it into a local database. The amount of sensor data and standalone computers is scalable. It is possible to have an unlimited amount of measuring systems that send encrypted data from each main server of an apartment to the main ‘FH-Server’ (Figure 1, right side). We have a 1:n relationship between a data receiving server and a scalable amount of data serving sensor computers in an apartment. Also, we could have a scalable amount of data collected in apartments, which provide sending encrypted data to the FH-Server by calling a special web service.

Figure 1

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Figure 1

Network topology of measuring sensor systems.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

The FH-Server hosts a web application for data collection, analysis and visualisation. The focus of backend application was an efficient communication and storage of all data types. The handling of large datasets and data analysis algorithms allows us to support big data amounts in our system. All developed backend applications use standard open source technologies of Java EE, such as EJB, JNDI, JPA, JAXB and JAX-RS. JAX-RS are used with RESTful web service interfaces to manage the communication. In the JSON-based client server communication via Web service for high security level, a cryptography support was implemented [Behrens et al. 2017].

To measure datasets in different apartments, a standalone measuring system based on Raspberry Pi with Debian Linux is used (figure 2). We use sensors to measure datasets of the relativity humidity RHin, pressure p, air temperature Tair, inside temperature of the outer wall Twall, inside temperature of the heating Theat, carbon dioxide level CO2 and window ventilation states STwindow, and A weather station measures the outside relativity humidity RHout, outside temperature Tout and outside wind speed Wout, as shown in Figure 2 [Behrens et al. 2017; Sonntag… 1990].

Figure 2

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Figure 2

Example of a measuring system in an apartment.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

To measure the personal room climate feelings, a visual display unit (figure 3) is located inside monitored apartment room. Residents reported their air climate feelings AQF by pressing one of three feedback buttons with smiley icons – green, yellow and red. Feeling states good AQFgood, neutral AQFneutral and bad AQFbad are generated, which are used for supervised machine learning algorithms.

Figure 3

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Figure 3

Visual display unit logging personal room climate feelings AQF.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

After pressing a Feedback button as input data, the user gets a view of actual measured air quality data. For training AIDIN, we interpreted indoor air quality based on a given DIN standard as summarised in Table 1.

Table 1

Ranges of sensor data as good, neutral and bad.

DIN standard ranges for winter
FeaturesUnitRange goodRange neutralRange bad
RHin[%]40684062<40>70
Tair[ºC]21232024<20>24
CO2[ppm]01,0001,0002,0002,000>2,000
Twall[ºC]13232335>13>35
DIN standard ranges for summer
FeaturesUnitRange goodRange neutralRange bad
RHin[%]40624055<40>70
Tair[ºC]23.5025.502226<22>26
CO2[ppm]01,0001,0002,0002,000>2,000
Twall[ºC]13232335>13>35

The German DIN standard [DIN EN ISO 7730; ASR… 2012; Detlef et al. 2013; DIN EN 13779] gives information about how the actual measurement data in a room should be (Table 1) to have a healthy room climate by interpreted categorisations ranges: AQFgood, AQFneutral and AQFbad. DIN standard deals with the sensors RHin [ASR… 2012], Tair [Detlef et al. 2013], CO2 [ASR… 2012; DIN EN 13779] and Twall [DIN EN ISO 7730]. Interpreting air quality state and room climate feeling as good, neutral or bad was made by sensor data fusion, getting multidimensional input feature vector to supervised learning algorithms AIDIN and AIextended. Sensor data fusion means that all records made by the measuring system are merged by matching the timestamp. The AIDIN algorithm is mainly used to train the neural network on almost all given datasets collected by sensors and then it serves as a base to learn the AIextended algorithm by extending itself. Finally, AIextended is based on the already learned AIDIN as a base.

This method allows the algorithm to learn air quality (AIDIN) from all measured datasets (high frequency by sensors) and to further correct itself as a new branch by learning air feelings (AIextended) of inhabitants (low frequency by input).

3 Results and discussion

Supervised machine learning comes with a data mining process described in this section. We collected data from apartments and laboratory. After collecting datasets, we started pre-processing by filtering them within matching timestamps, cleaning up faulty data and normalising them (Figure 4).

Figure 4

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Figure 4

Interpolation and normalisation within reciprocal transformations.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

After pre-processing, we made data reduction and data breakdown to get data according to heating season (winter) and the remaining data (summer). The measuring system collected datasets at a rate of every minute in the winter period and at a rate of every half-minute in the summer period. The ranges are given in Table 2. Overall, we have used 129,047 merged records in the winter period (approximately September to March) and 138,914 merged records in the summer period (approximately April to June) to train the algorithms described next.

Table 2

Overview of minima and maxima of collected measurement datasets for winter and summer season.

FeaturesUnitWinter rangesSummer ranges
STwindow[state]0.002.000.002.00
RHin[%]14.2777.1035.7167.45
p[hPa]960.331029.311006.131015.32
Tair[ºC]15.8928.6621.4828.93
Wout[km/h]0.0011.400.102.35
Tout[ºC]−10.5017.6014.7032.70
RHout[%]22.2099.0030.7086.20
CO2[ppm]350.006865.00400.001230.00
Theat[ºC]14.1354.4422.5628.94
Twall[ºC]8.5027.3124.1332.06
AQF[state]0.002.000.002.00

Basically, the overall supervised machine learning AIDIN and AIextended was made by using classified and randomly picked 70% of training data and 30% of testing data to create the AIs that are based on neural network approaches. The neural network used to train the algorithms was developed within this project and the chosen topology contained five layers. Between the first layer (an input layer) and the last layer (an output layer), we used three hidden layers, of which the first holds 8 neurons, the second holds 16 neurons and the third holds 8 neurons.

Input feature vector (STwindow, RHin, p, Tair, Wout, Tout, RHout, CO2, Theat and Twall) and target output vector (AQFgood, AQFneutral and AQFbad) were used, and a correlation matrix for each dataset is presented in Figures 5 and 6. The correlation matrix provides information about the relationship of sensor data to each other. The darker connection fields are more correlated than the lighter connection fields.

Figure 5

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Figure 5

Correlation matrix on winter datasets.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

Figure 6

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Figure 6

Correlation matrix on summer datasets.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

To differentiate the learned AIs, we named them by alias. AIDIN-winter learned DIN standard at heating season, AIextended-winter learned resident feelings at heating season, AIDIN-summer learned DIN standard at not heating season and AIextended-summer learned resident feeling at not heating season.

The first step was to create an intelligent AIDIN that learns the DIN standard air climate quality classes. The second step was to use the already learned AIDIN to create a second AIextended that was additionally based on the interpretation of personal room climate feeling states (AQFgood, AQFneutral and AQFbad). In the conclusion, we got two kinds of AIs, AIDIN and AIextended.

AIDIN represents the actual state of the air quality inside an apartment for healthy purpose and AIextended the actual state of personal feeling of an apartment resident. The classification experiments show the suitability of the learned algorithms for their determination (Figures 7 and 8).

Figure 7

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Figure 7

Comparing suitability of AIDIN-winter according to the DIN standard.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

Figure 8

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Figure 8

Comparing suitability of AIextended-winter according to the air feeling.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

The classification experiment 1 (winter) shows the algorithm AIDIN-winter, which has learned the air quality conditions, and it made 126614 correct interpretations towards 129047 given datasets.

The classification experiment 2 (winter) shows the extended algorithm AIextended-winter, which has learned the air feeling of residents, and it made 1,265 correct interpretations towards 1,274 given datasets.

Similar classification experiments (Figures 9 and 10) were performed on the not heating season (summer) by learning the AIDIN-summer and extended learning of the AIextended-summer. The classification experiment 1 (summer) shows the algorithm AIDIN-summer, which has learned the air quality conditions, and it made 126,614 correct interpretations towards 129,047 given datasets.

Figure 9

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Figure 9

Comparing suitability of AIDIN-summer according to the DIN standard.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

Figure 10

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Figure 10

Comparing suitability of AIextended-summer according to the air feeling.

Citation: Environmental Protection and Natural Resources; The Journal of Institute of Environmental Protection-National Research Institute. 29, 3; 10.2478/oszn-2018-0013

The classification experiment 2 (summer) shows the extended algorithm AIextended-summer, which has learned the air feeling of residents, and it made 1,265 correct interpretations towards 1,274 given datasets.

4 Conclusions

The developed smart monitoring system is based on RaspberryPi platform for measuring the air quality and wall humidity in apartment houses and delivers big sensor data. It recognises actual air quality in an apartment room by further telling us the actual personal air climate feeling of the residents.

In future works, a new academic research project based on the smart monitoring system developed in Bielefeld-Sennestadt for active assistance of the users during renovation process, called Environ and Approved, is needed. The benefit of this work is to use deep learned artificial intelligence for assistance purpose inside of apartments. Therefore, an apartment could be equipped with the algorithm AIDIN (as the base algorithm) to serve information about actual air quality in the beginning and the resident could teach the second algorithm AIextended (as a branch) about his/her personal air feelings by sending input data. In addition to information on measured data, residents will also receive suggestions of how to interact with their environment to get better room climate and to reduce CO2 emissions.

References and legal acts

  • BEHRENS G. et al. 2017. Smart Monitoring System of Air Quality and Wall Humidity Accompanying an Energy Efficient Renovation Process of Apartment Buildings. In: Otjacques B., Hitzelberger P., Naumann S., Wohlgemuth V. (eds) From Science to Society. Progress in IS. Springer, Cham, ISBN 978-3-319-65687-8: 181 – 189

  • SONNTAG, D. 1990. Important new values of the physical constants of 1986, vapour pressure formulations based on the IST-90, and psychrometer formulae. Z.Meteorol. 70 (5): 340-344

  • DIN DEUTSCHES INSTITUT FUER NORMUNG 2003. DIN EN ISO 7730 Ergonomie des Umgebungsklimas.

  • ASR A3.6 2012. Technische Regeln für Arbeitsstätten, www.baua.de/DE/Angebote/Rechtstexte-und-Technische-Regeln/Regelwerk/ASR/pdf/ASR-A3-6.pdf?__blob=publicationFile (state 08.05.2018)

  • DETLEF MAKULLA, DIETMAR ROSSBRUCH 2013. Auslegungskriterien für thermische Behaglichkeit, www.heinze.de/media/65228/pdf/19726555px595x842.pdf,(state 02.05.2018)

  • DIN DEUTSCHES INSTITUT FUER NORMUNG 2005, E. 13779. Lüftung von Nichtwohngebäuden–Allgemeine Grundlagen und Anforderungen an Lüftungs-und Klimaanlagen.

BEHRENS G. et al. 2017. Smart Monitoring System of Air Quality and Wall Humidity Accompanying an Energy Efficient Renovation Process of Apartment Buildings. In: Otjacques B., Hitzelberger P., Naumann S., Wohlgemuth V. (eds) From Science to Society. Progress in IS. Springer, Cham, ISBN 978-3-319-65687-8: 181 – 189

SONNTAG, D. 1990. Important new values of the physical constants of 1986, vapour pressure formulations based on the IST-90, and psychrometer formulae. Z.Meteorol. 70 (5): 340-344

DIN DEUTSCHES INSTITUT FUER NORMUNG 2003. DIN EN ISO 7730 Ergonomie des Umgebungsklimas.

ASR A3.6 2012. Technische Regeln für Arbeitsstätten, www.baua.de/DE/Angebote/Rechtstexte-und-Technische-Regeln/Regelwerk/ASR/pdf/ASR-A3-6.pdf?__blob=publicationFile (state 08.05.2018)

DETLEF MAKULLA, DIETMAR ROSSBRUCH 2013. Auslegungskriterien für thermische Behaglichkeit, www.heinze.de/media/65228/pdf/19726555px595x842.pdf,(state 02.05.2018)

DIN DEUTSCHES INSTITUT FUER NORMUNG 2005, E. 13779. Lüftung von Nichtwohngebäuden–Allgemeine Grundlagen und Anforderungen an Lüftungs-und Klimaanlagen.

Journal Information

CiteScore 2018: 0.41

SCImago Journal Rank (SJR) 2018: 0.143
Source Normalized Impact per Paper (SNIP) 2018: 0.387

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