Influence of User-Related Parameters on Calculated Energy Use in Low-Energy School Buildings

Simanic, Branko; Nordquist, Birgitta; Bagge, Hans; Johansson, Dennis

doi:10.3390/en13112985

Cited by 10 publications

(5 citation statements)

References 17 publications

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“…The ANN algorithm has been able to solve complexity problems in distribution network models dealing with combustion and circulation in plant levels, valves or dampers in distribution levels. In specific cases, they were tested by combining experimental models to respond various demands derived from different indoor and outdoor conditions [12][13][14]. In some algorithms, mixing methods of dampers and resistance coils were tested through the experimental data and data-driven regression to rapidly react thermal demands connecting some different building geometries and climate conditions.…”

Section: Building Thermal Controlsmentioning

confidence: 99%

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Ahn

2020

Sustainability

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For the sustainable use of building spaces, various methods have been studied to satisfy specific conditions required by the characteristics of space types and the energy use in operation. However, several effective control approaches adopting the latest statistical tools may have problems such as higher control precision increases energy consumption, or lower energy consumption decreases their control precision. This study proposes an optimized model to reach the indoor set-point temperature by controlling the amount of heating supply air and its temperature and investigates the efficiency of an adaptive controller to maintain indoor thermal comfort within setting ranges. In the consistency of the comfort level, the fuzzy logic controller was found to be 1.76% and the artificial neural network controller to be 17.83%, respectively, more efficient than the conventional thermostat. In addition, for energy use efficiency, both of the controllers were confirmed to be over 3.0% more efficient. Consequently, the network-based controller with the adaptive controller checking comfort levels effectively works to improve both energy efficiency and thermal comfort. This improvement can be significant in places such as commercial high-rises, large hospitals, and data centers where many spaces are intensively woven with appropriate thermal environments to maintain users’ workability.

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Section: Building Thermal Controlsmentioning

confidence: 99%

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Ahn

2020

Sustainability

View full text Add to dashboard Cite

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“…Regarding various operational strategies in combustion and circulation of fuel depending on the types of energy systems in buildings, the ANN algorithm has been preferred to generate signals for sensitive controls of valves or dampers. The models based on the ANN were compared to traditional systems, and tested by means of combining theoretical rules responding to characterized conditions derived from various factors in actual buildings [12,13]. As effective components of dampers and resistance coils, they were examined to build auxiliary systems complementing main HVAC systems with newly analyzing multivariate regression models derived from situational and seasonal operation results.…”

Section: Thermal Systems In Buildingsmentioning

confidence: 99%

Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

Ahn

2020

Sustainability

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In thermal controls in buildings, recent statistical and data-driven approaches to optimize supply air conditions have been examined in association with several types of building spaces and patterns of energy consumption. However, many strategies may have some problems where high-control precision may increase energy use, or low energy use in systems may decrease indoor thermal quality. This study investigates a neural network algorithm with an adaptive model on how to control the supply air conditions reflecting learned data. During the process, the adaptive model complements the signals from the network to independently maintain the comfort level within setting ranges. Although the proposed model effectively optimizes energy consumption and supply air conditions, it achieves quite improved comfort levels about 14% more efficient than comparison models. Consequently, it is confirmed that a network and learning algorithm equipped with an adaptive controller properly responds to users’ comfort levels and system’s energy consumption in a single space. The improved performance in space levels can be significant in places where many spaces are systematically connected, and in places which require a high consistency of indoor thermal comfort. Another advantage of the proposed model is that it properly reduces an increase in energy consumption despite an intensive strategy is utilized to improve thermal comfort.

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“…Geraldi and Ghisi [5] identified EPG as one of the four key building energy performance-related research topics. The causes of the EPG can be found in different life cycle stages of the building -design, construction and operation stages and many scientists emphasized the importance occupants' behaviour -both passive and active [4], [6]- [9]. According to Peper & Feist [10], occupants behaviour influence on actual energy consumption is ±50 %, therefore it should not be underestimated.…”

Section: Introductionmentioning

confidence: 99%

Office Building’s Occupancy Prediction Using Extreme Learning Machine Model with Different Optimization Algorithms

Motuzienė

Bielskus

Lapinskienė

et al. 2021

Environmental and Climate Technologies

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Increasing energy efficiency requirements lead to lower energy consumption in buildings, but at the same time occupants’ influence on the energy balance of the building during the use phase becomes more crucial. The randomness of the building’s occupancy often leads to the mismatch of the predicted and measured energy demand, also called Energy Performance Gap. Therefore, prediction of occupancy is important both in the design and use phases of the building. The goal of the study is to apply Extreme Learning Machine (ELM) models with different optimisation algorithms – Genetic (GA-ELM) and Simulated Annealing (SA–ELM) for occupancy prediction in an office building based on measured CO2 concentrations. Both models show similar and high accuracy of prediction: R2 – 0.73–0.74 and RMSE – 1.8–1.9 for the whole measured period. Influence of population size, number of neurons, and number of iterations on results accuracy was also analysed and recommendations are given. It was concluded that both methods are suitable for occupancy prediction, but because of different simulation times, SA-ELM is recommended for the Building Management Systems (BMS), where higher speed is required.

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Influence of User-Related Parameters on Calculated Energy Use in Low-Energy School Buildings

Cited by 10 publications

References 17 publications

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Performance Analyses of Temperature Controls by a Network-Based Learning Controller for an Indoor Space in a Cold Area

Improvement of the Performance Balance between Thermal Comfort and Energy Use for a Building Space in the Mid-Spring Season

Office Building’s Occupancy Prediction Using Extreme Learning Machine Model with Different Optimization Algorithms

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