IoT Solution Approach for Energy Consumption Reduction in Buildings : Part 3. Mathematical Model of Building and Experimental Results

Avotiņš, Ansis; Šenfelds, Armands; Ņikitenko, Agris; Podgornovs, Andrejs; Zadeiks, Kristaps; Dzenis, Mārcis

doi:10.1109/rtucon.2018.8659890

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Differently from other LPWANs, LoRaWAN is an open standard, supported by an alliance in charge of drafting standards, with many manufacturers offering development solutions. Not surprisingly, very different application scenarios have been addressed in the market and in the literature, including precision agriculture [47,48], electric vehicles management [49,50], industry [51,52], buildings [53,54], and finally, smart campus [55,56].…”

Section: Appendix a The Lorawan Communication Solutionsmentioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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The application of the most recent advances of the Internet-of-Things (IoT) technology to the automation of buildings is emerging as a promising solution to achieve greater efficiencies in energy consumption, and to allow the realization of sustainable models. The application of IoT has been demonstrated as effective in many fields, such as confirmed, for instance, by the Industry 4.0 concepts, which are revolutionizing modern production chains. By following this approach, the use of distributed control architectures and of IoT technologies (both wired and wireless) would result in effective solutions for the management of smart environments composed of groups of buildings, such as campuses. In this case, heterogeneous IoT solutions are typically adopted to satisfy the requirements of the very diverse possible scenarios (e.g., indoor versus outdoor coverage, mobile versus fixed nodes, just to mention a few), making their large-scale integration cumbersome. To cope with this issue, this paper presents an IoT architecture able to transparently manage different communication protocols in smart environments, and investigates its possible application for the monitoring and control of distributed energy resources in a smart campus. In particular, a use–case focused on the integration of the Long Range Wide Area Network (LoRaWAN) technology is considered to cope with heterogeneous indoor and outdoor communication scenarios. The feasibility analysis of the proposed solution is carried out by computing the scalability limits of the approach, based on the proposed smart campus data model. The results of the study showed that the proposed solution would be able to manage more than 10,000 nodes. An experimental validation of the LoRaWAN technology confirms its suitability in terms of coverage and latency, with a minimum LoRaWAN cell coverage range of 250 m, and a communication latency of about 400 ms. Finally, the advantages of the proposed solution in the supervision and management of a PV system are highlighted in a real-world scenario.

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Section: Appendix a The Lorawan Communication Solutionsmentioning

confidence: 99%

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

et al. 2020

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“…The progress of these sensors before 2016 has been surveyed in our previous paper [1]. Between 2016 to 2019, oxygen sensors [2], internet of things (IoT) frameworks with conventional sensors [3,4], particle counters, CO, NO 2 sensors [5], smart sensors incorporating a microprocessor chip [6], visual sensors [7], thermal flow sensors [8] and infra-red (IR) cameras [9] have been utilized to detect the thermal comfort, occupancy preference, and to eliminate measurement errors and reduce energy consumption. The detection of information by developed sensors will be fed back to the HVAC system for compensating the error between the set values and the measured ones.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results

Cheng

Lee

2019

Sensors

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The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.

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Selection of Key Parameters for Energy Management of Building Heating Systems

Dzelzītis

Zadeiks

Avotiņš

et al. 2019

2019 Nordic Workshop on Power and Industrial Electronics (NORPIE)

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IoT Solution Approach for Energy Consumption Reduction in Buildings : Part 3. Mathematical Model of Building and Experimental Results

Cited by 4 publications

References 12 publications

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

On the Use of LoRaWAN for the Monitoring and Control of Distributed Energy Resources in a Smart Campus

Artificial Intelligence Assisted Heating Ventilation and Air Conditioning Control and the Unmet Demand for Sensors: Part 2. Prior Information Notice (PIN) Sensor Design and Simulation Results

Selection of Key Parameters for Energy Management of Building Heating Systems

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