Activity-aware HVAC power demand forecasting

Cardoso, Enric Sala; Delgado-Prieto, Miguel; Kampouropoulos, Konstantinos; Romeral, L.

doi:10.1016/j.enbuild.2018.03.087

Cited by 33 publications

(11 citation statements)

References 34 publications

(36 reference statements)

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“…Cardoso et al (2018) [47] introduced a study of HVAC power-demand forecasting based on occupant activity. This influences our study in terms of the use of real data from a research building for estimation.…”

Section: Adaptive-predictive Control Strategiesmentioning

confidence: 99%

Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings

Yayla

Świerczewska²,

Kaya³

et al. 2022

Sustainability

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Buildings are responsible for almost half of the world’s energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves.

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Section: Adaptive-predictive Control Strategiesmentioning

confidence: 99%

Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings

Yayla

Świerczewska²,

Kaya³

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…Support Vector Machine (Cao et al, 2017) 1 100% Chen, Yeh, et al, 2012;Chen, Kuo, et al, 2012;Claveria et al, 2016;Hanmandlu and Chauhan, 2011;Hribar et al, 2019;Huarng et al, 2012;Jiang et al, 2018;Joseph et al, 2013;Khosravi and Nahavandi, 2014;Lou and Dong, 2013;Nose-Filho et al, 2011;Percy et al, 2018;Quan et al, 2014;Sala-Cardoso et al, 2018;Tiwari and Adamowski, 2017b;Xu et al, 2018;Yao et al, 2018;Ye et al, 2012;Yu et al, 2017) 21 27%…”

Section: Service-oriented Manufacturing Demand 1 4%mentioning

confidence: 99%

Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting

Aamer

Yani

Priyatna

2020

OSCM: An Int. Journal

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“…Several research streams recently dealt with the efficient operation of equipment and energy resources in smart buildings, by investigating different fields. Examples are the optimization of HVAC systems based on Model Predictive Control (MPC) algorithms or Neural Networks (NNs) [14][15][16][17], or the application of flexible control strategies, Demand Side Management (DSM) actions, and Demand Response (DR) programs [2,[18][19][20]. The application of such control strategies is implemented in single family houses through the so-called Home Energy Management Systems (HEMSs), such as well as in large buildings, by means of Building Energy Management Systems (BEMSs).…”

Section: Distributed Management Of Energy Resourcesmentioning

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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Activity-aware HVAC power demand forecasting

Abstract: Portal del coneixement obert de la UPC http://upcommons.upc.edu/e-prints Aquesta és una còpia de la versió author's final draft d'un article publicat a la revista Energy Policy.

Cited by 33 publications

References 34 publications

Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings

Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings

Data Analytics in the Supply Chain Management: Review of Machine Learning Applications in Demand Forecasting

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

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