Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam

Mastrucci, Alessio; Baume, Olivier; Stazi, Francesca; Leopold, Ulrich

doi:10.1016/j.enbuild.2014.02.032

Cited by 202 publications

(83 citation statements)

References 21 publications

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“…Among these, MR is a simple, reliable and a quick technique [11][12][13][14]. A number of researchers [11,12,[15][16][17][18][19][20][21][22][23][24] have used the MR method in their studies, but all such MR models forecast energy consumption of a single building or a region and require a lot of input data. Energy managers and their teams have always busy schedule and they would prefer a reliable and quick single model for different building categories instead of different forecasting models [24].…”

Section: Yearmentioning

confidence: 99%

Energy Consumption Forecasting for University Sector Buildings

et al. 2017

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Reliable energy forecasting helps managers to prepare future budgets for their buildings. Therefore, a simple, easier, less time consuming and reliable forecasting model which could be used for different types of buildings is desired. In this paper, we have presented a forecasting model based on five years of real data sets for one dependent variable (the daily electricity consumption) and six explanatory variables (ambient temperature, solar radiation, relative humidity, wind speed, weekday index and building type). A single mathematical equation for forecasting daily electricity usage of university buildings has been developed using the Multiple Regression (MR) technique. Data of two such buildings, located at the Southwark Campus of London South Bank University in London, have been used for this study. The predicted test results of MR model are examined and judged against real electricity consumption data of both buildings for year 2011. The results demonstrate that out of six explanatory variables, three variables; surrounding temperature, weekday index and building type have significant influence on buildings energy consumption. The results of this model are associated with a Normalized Root Mean Square Error (NRMSE) of 12% for the administrative building and 13% for the academic building. Finally, some limitations of this study have also been discussed.

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Section: Yearmentioning

confidence: 99%

Energy Consumption Forecasting for University Sector Buildings

et al. 2017

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“…Research on the energy renovations of dwellings usually focuses on selected cases (exemplary buildings) or case studies (Khoury et al 2016;Mastrucci et al 2014) except for a few dealing with epidimiological methods (Hamilton et al 2017). Up to now, and due to the difficulty of acquiring actual energy consumption data on big datasets, much of the research performed focused on the predicted energy savings of renovated building stocks (Ballarini et al 2014;Mata et al 2013).…”

Section: Energy Renovations and Savingsmentioning

confidence: 99%

Effectiveness of energy renovations: a reassessment based on actual consumption savings

2018

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Energy renovations offer unique opportunities to increase the energy efficiency of the built environment and for the existing housing stock; they are the most important solution. Usually, energy savings are based on modeling calculations. However, recent research has shown that the predicted energy consumption differs largely from the actual consumption. In this paper, the effectiveness of energy measures is reassessed based on actual consumption data. We use a monitoring system, which contains information about the energy performance of around 60% of the Dutch non-profit housing sector (circa 1.2 million dwellings). We connect the data from this monitoring system to actual energy consumption data from Statistics Netherlands on a dwelling level. Using longitudinal analysis methods, from 2010 to 2014, we are able to identify the energy efficiency improvements of the stock and determine the effectiveness of different measures in terms of actual energy savings. The results reveal the actual energy savings of different efficiency measures and highlight the significance of the actual energy consumption when a renovation is planned or realized.

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“…For example, the information about the year of construction of a building is useful in applications such as urban planning (Dalmau et al, 2014). Most prominently, it is relied heavily on in energy demand estimation (Mastrucci et al, 2014;Nouvel et al, 2015;Agugiaro, 2016a;Krüger and Kolbe, 2012), as it may serve as a proxy for their energy efficiency. Some researchers also include the year of refurbishment in their simulations (Agugiaro, 2016a).…”

Section: Building Age / Year Of Constructionmentioning

confidence: 99%

Estimating Building Age With 3d Gis

Biljecki

Sindram

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Building datasets (e.g. footprints in OpenStreetMap and 3D city models) are becoming increasingly available worldwide. However, the thematic (attribute) aspect is not always given attention, as many of such datasets are lacking in completeness of attributes. A prominent attribute of buildings is the year of construction, which is useful for some applications, but its availability may be scarce. This paper explores the potential of estimating the year of construction (or age) of buildings from other attributes using random forest regression. The developed method has a two-fold benefit: enriching datasets and quality control (verification of existing attributes). Experiments are carried out on a semantically rich LOD1 dataset of Rotterdam in the Netherlands using 9 attributes. The results are mixed: the accuracy in the estimation of building age depends on the available information used in the regression model. In the best scenario we have achieved predictions with an RMSE of 11 years, but in more realistic situations with limited knowledge about buildings the error is much larger (RMSE = 26 years). Hence the main conclusion of the paper is that inferring building age with 3D city models is possible to a certain extent because it reveals the approximate period of construction, but precise estimations remain a difficult task.

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Estimating energy savings for the residential building stock of an entire city: A GIS-based statistical downscaling approach applied to Rotterdam

Cited by 202 publications

References 21 publications

Energy Consumption Forecasting for University Sector Buildings

Energy Consumption Forecasting for University Sector Buildings

Effectiveness of energy renovations: a reassessment based on actual consumption savings

Estimating Building Age With 3d Gis

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