scholarly journals Energy-Related Occupant Behaviour and Its Implications in Energy Use: A Chronological Review

2018 ◽  
Vol 10 (8) ◽  
pp. 2635 ◽  
Author(s):  
Vivian Tam ◽  
Laura Almeida ◽  
Khoa Le
Keyword(s):  
Energy Use ◽  
Building Energy ◽  
Energy Performance ◽  
Design Stage ◽  
Occupant Behaviour ◽  
Building Energy Use ◽  
Key Variables ◽  
The Difference ◽  
The One ◽  
Building Life Cycle

It is essential to understand how significantly occupants’ actions impact the performance of a building, as a whole, in terms of energy use. Consequently, this paper reviews the available resources on energy-related occupant behaviour and its implications in energy use in a building. A chronological review on energy-related occupant behaviour and its implications in energy use has been conducted. As a main existing gap, it was identified by researchers the difference between real energy performance and the one that is predicted during the design stage of a building. The energy predicted during the design stage of a building may be over twice the energy used in the operation stage. Buildings are one of the most energy intensive features in a country. They are affected by the interaction and correlation of several different variables, such as: its physical characteristics, technical systems, equipment, occupants, etc. Therefore, buildings are considered to be complex systems that require a careful and intensive analysis. Moreover, one of the key variables impacting real building energy use is occupant behaviour. The way occupants behave and their motivations are some of the main aspects that need to be considered in a building life-cycle.

scholarly journals Evaluation of Urban-Scale Building Energy-Use Models and Tools—Application for the City of Fribourg, Switzerland

2021 ◽  
Vol 13 (4) ◽  
pp. 1595
Author(s):  
Valeria Todeschi ◽  
Roberto Boghetti ◽  
Jérôme H. Kämpf ◽  
Guglielmina Mutani
Keyword(s):  
Machine Learning ◽  
Energy Use ◽  
Residential Buildings ◽  
Building Energy ◽  
Energy Performance ◽  
Space Heating ◽  
Engineering Model ◽  
Building Energy Use ◽  
The Impact ◽  
The City

Building energy-use models and tools can simulate and represent the distribution of energy consumption of buildings located in an urban area. The aim of these models is to simulate the energy performance of buildings at multiple temporal and spatial scales, taking into account both the building shape and the surrounding urban context. This paper investigates existing models by simulating the hourly space heating consumption of residential buildings in an urban environment. Existing bottom-up urban-energy models were applied to the city of Fribourg in order to evaluate the accuracy and flexibility of energy simulations. Two common energy-use models—a machine learning model and a GIS-based engineering model—were compared and evaluated against anonymized monitoring data. The study shows that the simulations were quite precise with an annual mean absolute percentage error of 12.8 and 19.3% for the machine learning and the GIS-based engineering model, respectively, on residential buildings built in different periods of construction. Moreover, a sensitivity analysis using the Morris method was carried out on the GIS-based engineering model in order to assess the impact of input variables on space heating consumption and to identify possible optimization opportunities of the existing model.

General Correlation of Building Energy Use Via Hybrid Genetic Programming/Genetic Algorithm

2018 ◽  
Vol 140 (4) ◽  
Author(s):  
Raed I. Bourisli ◽  
Mohammed A. Altarakma ◽  
Adnan A. AlAnzi
Keyword(s):  
Genetic Programming ◽  
Closed Form ◽  
Hybrid Algorithm ◽  
Energy Use ◽  
Building Energy ◽  
Simulation Engine ◽  
Building Energy Use ◽  
The Difference ◽  
Predicted Values ◽  
Specific Number

A hybrid algorithm that combines genetic programming (GP) and genetic algorithms (GAs) that deduce a closed-form correlation of building energy use is presented. Throughout the evolution, the terms, functions, and form of the correlation are evolved via the genetic program. Whenever the fitness of the best correlation stagnates for a specific number of GP generations, the GA optimizes the real-valued coefficients of each correlation in the population. When the GA, in turn, stagnates, correlations with optimized coefficients and powers are passed back to the GP for further search. The hybrid algorithm is applied to the problem of predicting energy use of a U-shape building. More than 800 buildings with various foot-print areas, relative compactness (RC), window-to-wall ratio (WWR), and projection factor (PF) values were simulated using the VisualDOETM energy simulation engine. The algorithm tries to minimize the difference between simulated and predicted values by maximizing the R2 value. The algorithm was able to arrive at a closed-form correlation that combines the four building parameters, accurate to within 4%. The methodology can be easily used to model any type of data behavior in any engineering or nonengineering application.

scholarly journals Big-data for building energy performance: Lessons from assembling a very large national database of building energy use

2015 ◽  
Vol 140 ◽  
pp. 85-93 ◽  
Author(s):  
Paul A. Mathew ◽  
Laurel N. Dunn ◽  
Michael D. Sohn ◽  
Andrea Mercado ◽  
Claudine Custudio ◽  
...  
Keyword(s):  
Big Data ◽  
Energy Use ◽  
Building Energy ◽  
Energy Performance ◽  
National Database ◽  
Building Energy Performance ◽  
Building Energy Use ◽  
Large National Database

scholarly journals Occupant behaviour lifestyles and effects on building energy use: Investigation on high and low performing building features

2017 ◽  
Vol 140 ◽  
pp. 93-101 ◽  
Author(s):  
Verena M. Barthelmes ◽  
Cristina Becchio ◽  
Valentina Fabi ◽  
Stefano P. Corgnati
Keyword(s):  
Energy Use ◽  
Building Energy ◽  
Occupant Behaviour ◽  
Building Energy Use

scholarly journals Insights into the effects of occupant behaviour lifestyles and building automation on building energy use

2017 ◽  
Vol 140 ◽  
pp. 48-56 ◽  
Author(s):  
Valentina Fabi ◽  
Verena M. Barthelmes ◽  
Marcel Schweiker ◽  
Stefano P. Corgnati
Keyword(s):  
Energy Use ◽  
Building Energy ◽  
Building Automation ◽  
Occupant Behaviour ◽  
Building Energy Use

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

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2985 ◽  
Author(s):  
Branko Simanic ◽  
Birgitta Nordquist ◽  
Hans Bagge ◽  
Dennis Johansson
Keyword(s):  
Energy Use ◽  
Ventilation System ◽  
Building Energy ◽  
Energy Performance ◽  
Hot Water ◽  
Low Energy ◽  
Building Energy Use ◽  
Low Energy Buildings ◽  
Demand Controlled Ventilation ◽  
The Impact

Literature and experience show that there are large discrepancies between the calculated and measured building energy usages, where user-related parameters are significant factors with regard to energy use in low-energy buildings. Furthermore, the difficulties encountered when quantifying these parameters compound these discrepancies. The main aim of this study was to provide feedback that would help the building industry and research communities to predict more accurately the impact of the user-related parameters on energy performance. The results of the study would, subsequently, contribute to minimizing the discrepancies between calculated and measured energy use. This article analyses simulated building energy use based on randomly chosen combinations of measured user-related parameters in three recently built low-energy schools in Sweden. The results show that energy performance can span from 30 to 160 kWh/(m² y) simply by varying the combination of previously measured user-related parameters in building energy simulations. The study shows that the set points for indoor air temperatures during the heating season and the energy required to run a demand-controlled ventilation system have an extensive influence, while tenant electricity use has a slightly lower influence on building energy use. Variations in occupancy rates and energy for hot water usage have the smallest influences on building energy use.

scholarly journals Minimising the Deviation between Predicted and Actual Building Performance via Use of Neural Networks and BIM

Buildings ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 131 ◽  
Author(s):  
Ahmed WA Hammad
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Building Energy ◽  
Energy Performance ◽  
Limiting Factor ◽  
Occupant Behaviour ◽  
Actual Performance ◽  
Building Energy Performance ◽  
The Difference ◽  
Actual Energy Consumption

Building energy performance tools are widely used to simulate the expected energy consumption of a given building during the operation phase of its life cycle. Deviations between predicted and actual energy consumptions have however been reported as a major limiting factor to the tools adopted in the literature. A significant reason highlighted as greatly influencing the difference in energy performance is related to the occupant behaviour of the building. To enhance the effectiveness of building energy performance tools, this study proposes a method which integrates Building Information Modelling (BIM) with artificial neural network model for limiting the deviation between predicted and actual energy consumption rates. Through training a deep neural network for predicting occupant behaviour that reflects the actual performance of the building under examination, accurate BIM representations are produced which are validated via energy simulations. The proposed method is applied to a realistic case study, which highlights significant improvements when contrasted with a static simulation that does not account for changes in occupant behaviour.

scholarly journals The ambivalence of personal control over indoor climate – how much personal control is adequate?

2020 ◽  
Vol 172 ◽  
pp. 06010
Author(s):  
Runa T. Hellwig ◽  
Marcel Schweiker ◽  
Atze Boerstra
Keyword(s):  
User Satisfaction ◽  
Perceived Control ◽  
Energy Use ◽  
Sick Building Syndrome ◽  
Personal Control ◽  
Building Energy ◽  
Energy Performance ◽  
Indoor Climate ◽  
Building Energy Use ◽  
Automation Systems

Literature sets personal control over indoor environmental conditions in relation to the gap between predicted and actual energy use, the gap between predicted and observed user satisfaction, and health aspects. A focus on building energy performance often leads to the proposal of more automated and less occupant control of the indoor environment. However, a high degree of personal control is desirable because research shows that a low degree (or no) personal control highly correlates with indoor environmental dissatisfaction and sick building syndrome symptoms. These two tendencies seem contradictory and optimisation almost impossible. Based on current efficiency classes describing the effect of room automation systems on building energy use during operation, fundamental thoughts related to thermophysiology and control, recent laboratory experiments, important lessons learnt from post-occupancy studies, and documented conceptual frameworks on the level of control perceived, we discuss the ambivalence of personal control and how much personal control is adequate. Often-proposed solutions ranging from fully automated controls, over manual controls to dummy controls are discussed according to their effect on a) building energy use during operation and b) occupants perceived control. The discussion points to the importance of adequate personal control. In order to meet the goals for nearly zero energy buildings and for a human-centric design, there is the need to establish design procedures for adequate personal control as part of the design process.

A Comparative Study on Building Energy Performance between Movable and Fixed Shading Materials

2012 ◽  
Vol 164 ◽  
pp. 85-88
Author(s):  
Xiao Chang Yang ◽  
Jian Yao
Keyword(s):  
Energy Use ◽  
Residential Building ◽  
Building Energy ◽  
Energy Performance ◽  
Simulation Software ◽  
Public Building ◽  
Building Energy Performance ◽  
Energy Demands ◽  
Building Energy Use ◽  
Better Than

The purpose of this paper is to investigate the effect of movable shading on building energy demands. A public building and a residential building with movable shading and fixed shading were modeled by the building energy use simulation software DOE-2. Cooling and heating energy demands were calculated. The results showed that movable shading is better than fixed shading.

scholarly journals Energy and Greenhouse Gas Savings for LEED-Certified U.S. Office Buildings

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 749
Author(s):  
John H. Scofield ◽  
Susannah Brodnitz ◽  
Jakob Cornell ◽  
Tian Liang ◽  
Thomas Scofield
Keyword(s):  
Greenhouse Gas ◽  
Energy Use ◽  
Energy Savings ◽  
Electric Energy ◽  
Building Energy ◽  
Energy Performance ◽  
Office Buildings ◽  
Ghg Emission ◽  
Site Energy ◽  
New Construction

In this work, we present results from the largest study of measured, whole-building energy performance for commercial LEED-certified buildings, using 2016 energy use data that were obtained for 4417 commercial office buildings (114 million m2) from municipal energy benchmarking disclosures for 10 major U.S. cities. The properties included 551 buildings (31 million m2) that we identified as LEED-certified. Annual energy use and greenhouse gas (GHG) emission were compared between LEED and non-LEED offices on a city-by-city basis and in aggregate. In aggregate, LEED offices demonstrated 11% site energy savings but only 7% savings in source energy and GHG emission. LEED offices saved 26% in non-electric energy but demonstrated no significant savings in electric energy. LEED savings in GHG and source energy increased to 10% when compared with newer, non-LEED offices. We also compared the measured energy savings for individual buildings with their projected savings, as determined by LEED points awarded for energy optimization. This analysis uncovered minimal correlation, i.e., an R2 < 1% for New Construction (NC) and Core and Shell (CS), and 8% for Existing Euildings (EB). The total measured site energy savings for LEED-NC and LEED-CS was 11% lower than projected while the total measured source energy savings for LEED-EB was 81% lower than projected. Only LEED offices certified at the gold level demonstrated statistically significant savings in source energy and greenhouse gas emissions as compared with non-LEED offices.

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