摘要：The energy performance assessment of buildings during design is usually based on energy simulations with pre-defined input data from standards and legislations. Typically, the internal gain values and profiles are based on EN 16798–1. However, studies have shown that the real electricity use of plug load and lighting varies more smoothly than in the profiles of EN 16798–1 where zero occupancy outside working hours is assumed. This might result in sub-optimal building solutions due to inadequate building performance simulation input data. The aim of this work is to structure and analyse data from a total of 196 electricity meters in 4 large office buildings in Tallinn, Estonia. Typically, 3 to 8 electricity meters were installed per floor with the consumption coming mainly from plug loads and electric lighting. The data had been gathered between the years 2016–2020 with either 1 or 24 hour time steps, depending on the building and the electricity meter. 3 out of the 4 buildings had an average normalized energy usage slightly below the modelling value calculated according to EN16798–1. Some office spaces stood out with an abnormally high electricity consumption, however, the 24-hour distributions were fairly compact, meaning quite steady consumption patterns. When looking at the dispersion of energy consumption per 24h, averaged over all given offices in a building, no outliers stood out, either. This means that there are not many days when the average consumption and internal heat gains of all offices were simultaneously well below the mean. Additionally, major events like holidays and the COVID19-induced lockdown show up well on the graphs, but also planned changes in occupancy can be seen.
其他摘要：The energy performance assessment of buildings during design is usually based on energy simulations with pre-defined input data from standards and legislations. Typically, the internal gain values and profiles are based on EN 16798–1. However, studies have shown that the real electricity use of plug load and lighting varies more smoothly than in the profiles of EN 16798–1 where zero occupancy outside working hours is assumed. This might result in sub-optimal building solutions due to inadequate building performance simulation input data. The aim of this work is to structure and analyse data from a total of 196 electricity meters in 4 large office buildings in Tallinn, Estonia. Typically, 3 to 8 electricity meters were installed per floor with the consumption coming mainly from plug loads and electric lighting. The data had been gathered between the years 2016–2020 with either 1 or 24 hour time steps, depending on the building and the electricity meter. 3 out of the 4 buildings had an average normalized energy usage slightly below the modelling value calculated according to EN16798–1. Some office spaces stood out with an abnormally high electricity consumption, however, the 24-hour distributions were fairly compact, meaning quite steady consumption patterns. When looking at the dispersion of energy consumption per 24h, averaged over all given offices in a building, no outliers stood out, either. This means that there are not many days when the average consumption and internal heat gains of all offices were simultaneously well below the mean. Additionally, major events like holidays and the COVID19-induced lockdown show up well on the graphs, but also planned changes in occupancy can be seen.
