skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London

Abstract

This paper describes a method for predicting air temperatures within the Urban Heat Island at discreet locations based on input data from one meteorological station for the time the prediction is required and historic measured air temperatures within the city. It uses London as a case-study to describe the method and its applications. The prediction model is based on Artificial Neural Network (ANN) modelling and it is termed the London Site Specific Air Temperature (LSSAT) predictor. The temporal and spatial validity of the model was tested using data measured 8 years later from the original dataset; it was found that site specific hourly air temperature prediction provides acceptable accuracy and improves considerably for average monthly values. It thus is a very reliable tool for use as part of the process of predicting heating and cooling loads for urban buildings. This is illustrated by the computation of Heating Degree Days (HDD) and Cooling Degree Hours (CDH) for a West-East Transect within London. The described method could be used for any city for which historic hourly air temperatures are available for a number of locations; for example air pollution measuring sites, common in many cities, typically measure air temperature on an hourlymore » basis. (author)« less

Authors:
;  [1]; ; ;  [2]
  1. Mechanical Engineering, School of Engineering and Design, Brunel University, Uxbridge (United Kingdom)
  2. The Bartlett School of Graduate Studies, University College London (United Kingdom)
Publication Date:
OSTI Identifier:
21396197
Resource Type:
Journal Article
Journal Name:
Solar Energy
Additional Journal Information:
Journal Volume: 84; Journal Issue: 12; Other Information: Elsevier Ltd. All rights reserved; Journal ID: ISSN 0038-092X
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; AIR; HEAT ISLANDS; FORECASTING; COOLING; ENERGY DEMAND; HEATING; BUILDINGS; URBAN AREAS; UNITED KINGDOM; DEGREE DAYS; CALCULATION METHODS; NEURAL NETWORKS; ACCURACY; SIMULATION; HEATING LOAD; COOLING LOAD

Citation Formats

Kolokotroni, Maria, Bhuiyan, Saiful, Davies, Michael, Croxford, Ben, and Mavrogianni, Anna. A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London. United States: N. p., 2010. Web. doi:10.1016/J.SOLENER.2010.08.002.
Kolokotroni, Maria, Bhuiyan, Saiful, Davies, Michael, Croxford, Ben, & Mavrogianni, Anna. A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London. United States. https://doi.org/10.1016/J.SOLENER.2010.08.002
Kolokotroni, Maria, Bhuiyan, Saiful, Davies, Michael, Croxford, Ben, and Mavrogianni, Anna. 2010. "A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London". United States. https://doi.org/10.1016/J.SOLENER.2010.08.002.
@article{osti_21396197,
title = {A validated methodology for the prediction of heating and cooling energy demand for buildings within the Urban Heat Island: Case-study of London},
author = {Kolokotroni, Maria and Bhuiyan, Saiful and Davies, Michael and Croxford, Ben and Mavrogianni, Anna},
abstractNote = {This paper describes a method for predicting air temperatures within the Urban Heat Island at discreet locations based on input data from one meteorological station for the time the prediction is required and historic measured air temperatures within the city. It uses London as a case-study to describe the method and its applications. The prediction model is based on Artificial Neural Network (ANN) modelling and it is termed the London Site Specific Air Temperature (LSSAT) predictor. The temporal and spatial validity of the model was tested using data measured 8 years later from the original dataset; it was found that site specific hourly air temperature prediction provides acceptable accuracy and improves considerably for average monthly values. It thus is a very reliable tool for use as part of the process of predicting heating and cooling loads for urban buildings. This is illustrated by the computation of Heating Degree Days (HDD) and Cooling Degree Hours (CDH) for a West-East Transect within London. The described method could be used for any city for which historic hourly air temperatures are available for a number of locations; for example air pollution measuring sites, common in many cities, typically measure air temperature on an hourly basis. (author)},
doi = {10.1016/J.SOLENER.2010.08.002},
url = {https://www.osti.gov/biblio/21396197}, journal = {Solar Energy},
issn = {0038-092X},
number = 12,
volume = 84,
place = {United States},
year = {Wed Dec 15 00:00:00 EST 2010},
month = {Wed Dec 15 00:00:00 EST 2010}
}