Implementation of predictive control in a commercial building energy management system using neural networks

Macarulla, Marcel; Casals, Miquel; Forcada, Núria; Gangolells, Marta

doi:10.1016/j.enbuild.2017.06.027

Cited by 62 publications

(46 citation statements)

References 33 publications

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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%

“…In the latter case, advanced control functions are usually defined by supervisory levels and then communicated to local controllers and actuators by means of local networks. Conversely, the implementation of DSM actions and DR programs, which involves the participation of third-party agents, such as Distribution System Operators (DSOs) or independent aggregators, requires the use of WANs [7,17].…”

Section: Distributed Management Of Energy Resourcesmentioning

confidence: 99%

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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: Distributed Management Of Energy Resourcesmentioning

confidence: 99%

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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“…An equivalent method has been employed for setback moment determination of cooling system in other existing studies . Similarly, Macarulla et al tested a control strategy to determine the optimum starting time of an office building boiler to achieve thermal comfort at the beginning of each working day. They managed to save 20% of energy while guaranteeing thermal comfort.…”

Section: Neural Network Applications Over a Building's Lifementioning

confidence: 99%

Overview of the use of artificial neural networks for energy‐related applications in the building sector

et al. 2019

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Summary The incessant growing of the world's energy consumption and associated greenhouse gases emissions have created tremendous problems to be solved by today's and future generations. As the building sector is one of the biggest energy consumers, reducing its energy consumption is now mandatory. Being able to conceive and built efficient buildings, to effectively manage and operate them, and to rapidly renovate the existing building stock is a challenging task. Neural networks models open new possibilities to address this problem. This paper offers a comprehensive review of the studies that use neural networks for energy‐related applications in the building sector focusing on their application and on the technical characteristics of the network (ie, learning algorithm, number of layers, number of neurons, inputs and output variables, and performance criteria). On the basis of this review, limitations concerning the use of neural networks in the building sector along with existing research gaps and future research directions are identified.

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“…According to the study, the energy saving potential of a building to be 7.4 MWh/year. Macarulla et al [6] present the procedure of implementing a predictive control strategy in a commercial BEMS for boilers in buildings based on a neural network that turned on the boiler each day at the optimum time, according to the surrounding environment, to achieve thermal comfort levels at the beginning of the working day. The results showed that the implementation of predictive control in a BEMS for building boilers could reduce the energy required to heat the building by around 20% without compromising the user's comfort.…”

Section: Literature Reviewmentioning

confidence: 99%

The Design of Building Management Platform Based on Cloud Computing and Low-Cost Devices

Huang¹,

Chiu²,

Chan³

2019

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Indoor environment monitor and control are important aspects of reducing building energy consumption and maintaining occupants' visual and thermal comforts. Previous research showed that the lots of buildings were not able to provide designed service level in the daily operation and the feedback from occupants showed that the predetermined service level sometimes did not match occupants' desire. However, most of existing buildings and residential buildings did not have building management systems that can systematically monitor the indoor environment, collect occupants' feedback, and fine-tune built-in control logic when the occupants' needs could not be met. An easy-to-build and easy-to-use build management system can help researchers, engineers, and occupants themselves to identify the issue. The goal of this study was to develop a building management platform with the functions of indoor environment data collection (temperature, relative humidity, solar radiation, etc.), remote control (air conditioner, window, shading), occupants' feedback collection (set point, status of building components, etc.), and user interface for retrieving the data and modifying control logic. The platform was developed using Arduino-based and Raspberry Pi-based microcontroller board, low-cost sensors, 3D printing technology, and cloud computing technology. The stored data can serve for personal behavior/comfort analysis, components efficiency analysis, and advanced control logic development. This last part of this paper demonstrated the ability of the developed platform and exhibited the potential application. In the end, we provided further discussion about the potential challenges we might face when developing building management systems in other existing spaces/buildings.

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Implementation of predictive control in a commercial building energy management system using neural networks

Cited by 62 publications

References 33 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

Overview of the use of artificial neural networks for energy‐related applications in the building sector

The Design of Building Management Platform Based on Cloud Computing and Low-Cost Devices

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