A review of building climate and plant controls, and a survey of industry perspectives

Royapoor, Mohammad; Antony, Anu; Roskilly, Anthony Paul

doi:10.1016/j.enbuild.2017.10.022

Cited by 51 publications

(19 citation statements)

References 83 publications

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“…It is also caused by the difficulty to model nonphysically predictable factors like human behavior. The second key solution, ie, efficient building's energy systems management, is mandatory since complex energy production and distribution systems are nowadays widely used and classic control strategies have showed their limits when it comes to control those systems efficiently . Moreover, the development of renewable energies combined to their intermittent characteristic causes several problems regarding the electricity network management.…”

Section: Introductionmentioning

confidence: 99%

“…The second key solution, ie, efficient building's energy systems management, is mandatory since complex energy production and distribution systems are nowadays widely used and classic control strategies have showed their limits when it comes to control those systems efficiently. 5 Moreover, the development of renewable energies combined to their intermittent characteristic causes several problems regarding the electricity network management. That is why creating smart grids for an efficient energy network management constitutes the third solution, but it requires the development of smart-buildings with calculable and predictable energy consumptions.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…As HVAC systems contribute a major proportion of building energy consumption (e.g., 54% of electricity is consumed by space cooling in office segment of Hong Kong [ 1 ] and 39.6% in commercial buildings of US [ 2 ]), reducing their energy consumption is of great importance. However, as the cooling demand varies irregularly over time, it is very difficult to maintain an optimal operation of HVAC systems [ 3 , 4 ]. Many research studies have revealed that a demand-based control is a powerful tool to facilitate an optimal performance of the HVAC system [ 4 , 5 , 6 ] and recent studies reveal that the demand-based control can lead to energy savings from 10% to 60% [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, as the cooling demand varies irregularly over time, it is very difficult to maintain an optimal operation of HVAC systems [ 3 , 4 ]. Many research studies have revealed that a demand-based control is a powerful tool to facilitate an optimal performance of the HVAC system [ 4 , 5 , 6 ] and recent studies reveal that the demand-based control can lead to energy savings from 10% to 60% [ 7 ]. To realize such energy savings, it is necessary to determine the real-time cooling demand accurately and efficiently.…”

Section: Introductionmentioning

confidence: 99%

A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing

Wang

Tse

Poon

et al. 2018

Sensors

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The operating efficiency of heating, ventilation and air conditioning (HVAC) system is critical for building energy performance. Demand-based control is an efficient HVAC operating strategy, which can provide an appropriate level of HVAC services based on the recognition of actual cooling “demand.” The cooling demand primarily relies on the accurate detection of occupancy. The current researches of demand-based HVAC control tend to detect the occupant count using cameras or other sensors, which often impose high computation and costs with limited real-life applications. Instead of detecting the occupant count, this paper proposes to detect the occupancy density. The occupancy density (estimated by image foreground moving pixels) together with the indoor and outdoor information (acquired from existing sensors) are used as inputs to an artificial neural network model for cooling demand estimation. Experiments have been implemented in a university design studio. Results show that, by adding the occupancy density, the cooling demand estimation error is greatly reduced by 67.4% and the R value is improved from 0.75 to 0.96. The proposed approach also features low-cost, computationally efficient, privacy-friendly and easily implementable. It shows good application potentials and can be readily incorporated into existing building management systems for improving energy efficiency.

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“…Therefore, designers and researchers often turn to optimal or predictive solutions [9].However, advanced solutions are not easy to implement and the need for robust and reliable solutions with minimal human interaction is evident. [10,11]. To simplify or avoid technicians' inputs to the systems, the control algorithms can be tested in realistic environments [12].…”

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confidence: 99%

PI Parameter Influence on Underfloor Heating Energy Consumption and Setpoint Tracking in nZEBs

2020

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In rooms with underfloor heating (UFH), local on-off controllers most often regulate the air temperature with poor accuracy and energy penalties. It is known that proportional-integral (PI) controllers can regulate most processes more precisely. However, hydronic UFH systems have long time constants, especially in low-energy buildings, and PI parameters are not easy to set manually. In this work, several potential PI parameter estimation methods were applied, including optimizing the parameters in GenOpt, calculating the parameters based on simplified models, and tuning the parameters automatically in Matlab. For all found parameter combinations, the energy consumption and control precision were evaluated. Simpler methods were compared to the optimal solutions to find similar parameters. Compared with an on-off controller with a 0.5 K dead-band, the best PI parameter combination found was with a proportional gain of 18 and an integration time of 2300 s, which could decrease the energy consumption for heating by 9% and by 5% compared with default PI parameters. Moreover, while GenOpt was the best method to find the optimal parameters, it was also possible with a simple automatic test and calculation within a weekend. The test can be, for example, 6-h setbacks applied during the nights or weekend-long pseudo-random changes in the setpoint signal. The parameters can be calculated based on the simplified model from these tests using any well-known simple method. Results revealed that the UFH PI controller with the correct parameters started to work in a predictive fashion and the resulting room temperature curves were practically ideal.Energies 2020, 13, 2068 2 of 20 the input signal is, for example, the electricity price or other signal from the grid and the output of the optimization are the air temperature setpoints of the rooms [3].The setpoints in rooms have to be tracked by room-based controllers, such as thermostats or proportional-integral-derivative (PID) controllers, as the supervisory control can deal with optimization but not with the local fast changes [8]. PID is commonly known to be one of the best and easiest feedback controllers for any process. For buildings, as a relatively slow system, the derivative part is usually dropped and PI controllers are used instead. However, choosing improper PID gains (parameters) could result in making the whole system unstable. Therefore, designers and researchers often turn to optimal or predictive solutions [9].However, advanced solutions are not easy to implement and the need for robust and reliable solutions with minimal human interaction is evident. [10,11]. To simplify or avoid technicians' inputs to the systems, the control algorithms can be tested in realistic environments [12]. Also, building blocks have been developed to be compatible with detailed modelling so that engineers can do the tuning. However, the process is still not fully automated [13]. Auto-tuning of PID controllers for heating, cooling, and ventilation plants have been described both several ...

show abstract

A review of building climate and plant controls, and a survey of industry perspectives

Cited by 51 publications

References 83 publications

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

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

A Practical Multi-Sensor Cooling Demand Estimation Approach Based on Visual, Indoor and Outdoor Information Sensing

PI Parameter Influence on Underfloor Heating Energy Consumption and Setpoint Tracking in nZEBs

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