Clustering a building stock towards representative buildings in the context of air-conditioning electricity demand flexibility
| Autor: | Alessia Arteconi, Charles D. Corbin, Lieve Helsen, Gregor P. Henze, Dieter Patteeuw |
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| Rok vydání: | 2018 |
| Předmět: |
Energy management
business.industry Computer science 020209 energy 0211 other engineering and technologies Air-conditioning 02 engineering and technology Building and Construction Environmental economics Electricity demand Clustering Computer Science Applications Aggregation Demand flexibility Air conditioning Modeling and Simulation 021105 building & construction Architecture 0202 electrical engineering electronic engineering information engineering Building stock diversity Cluster analysis business Stock (geology) |
| Zdroj: | Journal of Building Performance Simulation. 12:56-67 |
| ISSN: | 1940-1507 1940-1493 |
| Popis: | Energy modeling for the prediction of energy use in buildings, especially under novel energy management strategies, is of great importance. In buildings there are several flexible electrical loads which can be shifted in time such as thermostatically controllable loads. The main novelty of this paper is to apply an aggregation method to effectively characterize the electrical energy demand of air-conditioning (AC) systems in residential buildings under flexible operation during demand response and demand shaping programs. The method is based on clustering techniques to aggregate a large and diverse building stock of residential buildings to a smaller, representative ensemble of buildings. The methodology is tested against a detailed simulation model of building stocks in Houston, New York and Los Angeles. Results show good agreement between the energy demand predicted by the aggregated model and by the full model during normal operation (normalized mean absolute error, NMAE, below 10%), even with a small number of clusters (sample size of 1%). During flexible operation, the normalized mean absolute error rises (around 20%) and a higher number of representative buildings becomes necessary (sample size at least 10%). Multiple cases for the input data series were considered, namely by varying the time resolution of the input data and the type of input data. These characteristics of the input time series data are shown to play a crucial role in the aggregation performance. The aggregated model showed lower NMAE compared to the original model when clustering is based on a hybrid signal resolved at 60-minute time intervals, which is a combination of the electricity demand profile and AC modulation level. ispartof: Journal Of Building Performance Simulation vol:12 issue:1 pages:56-67 status: published |
| Databáze: | OpenAIRE |
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