Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building

Tronchin, Lamberto; Manfren, Massimiliano; James, P.A.B.

doi:10.1016/j.energy.2018.09.037

Cited by 49 publications

(31 citation statements)

References 65 publications

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“…Various researchers have demonstrated that occupants affect the building energy consumption the most, and they are also the primary cause of the "Energy-performance Gap" [5][6][7][8]. Several previous studies have investigated the variation in energy consumption as a function of the actual occupant characteristics and activities.…”

Section: Effects Of Occupant Characteristics On Energy Consumptionmentioning

confidence: 99%

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

2019

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The evaluation of building energy consumption is heavily based on building characteristics and thus often deviates from the true consumption. Consequently, user-based estimation of building energy consumption is necessary because the actual consumption is greatly affected by user characteristics and activities. This work aims to examine the variation in energy consumption as a function of user activities within the same building, and to employ an artificial neural network (ANN) to predict user-based energy consumption. The study exploited the actual 24-h schedules of 5240 single-person households and computed the respective energy consumption using EnergyPlus V 8.8.0 software. The calculated values were clustered according to gender, age, occupation, income, educational level, and occupancy period and the difference among them was analyzed. The simulation results showed that for single-person households in Korea, females used more energy than males did, and the difference increased with age. Furthermore, unemployed and low-income individuals consumed more energy whereas consumption was inversely proportional to the educational level. Energy consumption increased with the occupancy period. Based on the simulation results and six user characteristics, the ANN model indicated a correlation between user characteristics and energy usage. This study analyzed the differences in energy usage depending on user activity and characteristics that affect building energy consumption.

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Section: Effects Of Occupant Characteristics On Energy Consumptionmentioning

confidence: 99%

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

2019

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“…In general, the U-value of building materials was treated as a categorical variable because it is a set value that does not vary over time. Based on these results, we concluded that the electricity of a building can be effectively managed if only four or five major energy variables derived from the building are controlled or used as key points of operation [53,54]. consumption.…”

Section: Research Resultsmentioning

confidence: 95%

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

et al. 2019

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Although the latest energy-efficient buildings use a large number of sensors and measuring instruments to predict consumption more accurately, it is generally not possible to identify which data are the most valuable or key for analysis among the tens of thousands of data points. This study selected the electric energy as a subset of total building energy consumption because it accounts for more than 65% of the total building energy consumption, and identified the variables that contribute to electric energy use. However, this study aimed to confirm data from a building using clustering in machine learning, instead of a calculation method from engineering simulation, to examine the variables that were identified and determine whether these variables had a strong correlation with energy consumption. Three different methods confirmed that the major variables related to electric energy consumption were significant. This research has significance because it was able to identify the factors in electric energy, accounting for more than half of the total building energy consumption, that had a major effect on energy consumption and revealed that these key variables alone, not the default values of many different items in simulation analysis, can ensure the reliable prediction of energy consumption.

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“…As a matter of fact, buildings are not only electricity consumers but also thermal energy ones. Coupling electrification and energy refurbishment, the actual integration of solar energy must be ensured from the pre-design to the operation phase [25]. Therefore, modelling and optimization should aim at cost-benefit analysis for renovation [26], together with the integration of Information & Communication Technologies (ICT) such as building automation [27].…”

Section: Introductionmentioning

confidence: 99%

Solar Energy Data Analytics: PV Deployment and Land Use

Mancini

Nastasi

2020

Energies

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EU targets for sustainable development call for strong changes in the current energy systems as well as committed protection of environmental resources. This target conflicts if a policy is not going to promote the compatible solutions to both the issues. This is the case of the additional renewable energy sources to be exploited for increasing the share in the electricity mix and in the gross final energy consumption. Solar energy is, currently, the cheapest solution in Southern European Countries, like Italy. In this paper, thanks to the availability of three open databases provided by National Institutions, the authors compared the historic trends and policy scenarios for soil consumption, electricity consumption, and renewable electricity production to check correlations. The provincial scale was chosen as resolution of the analysis. The deviations from the policy scenarios was then addressed to identify the demand for policy recommendations and pathways to promote in order to achieve the target for renewable electricity share as well as the reduction in soli consumption trend in 2030. The role of renewables integrated in the existing contexts, such as building integrated photovoltaics, is considered a key driver for solving this issue.

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Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building

Cited by 49 publications

References 65 publications

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

Prediction Model Based on an Artificial Neural Network for User-Based Building Energy Consumption in South Korea

Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach

Solar Energy Data Analytics: PV Deployment and Land Use

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