Estimating Energy Efficiency of Buildings using Neural Networks
Matt Wysocki
Project Proposal:
There has been a lot research targeted at developing ways to make buildings more
efficient with growing concerns of energy consumption and environmental impact. Heating and
cooling of buildings accounts for a significant percentage of their total energy usage. One way to
alleviate this cost would be to design and build more efficient buildings. To gain a better
understanding of what building parameters contribute to consumption of energy, an artificial
neural network can be applied to weight certain attributes of buildings and identify how these
parameters affect the heating and cooling load of a building.
Accurately predicting how building parameters affect heating and cooling loads on
buildings will aid in the design and of more efficient buildings. Various parameters will be input
into the ANN including: surface area, wall area, roof area, orientation, overall height, and several
others. Being able to predict building energy usage more accurately will allow for more informed
decisions when designing buildings and lead to construction of more efficient buildings.
For my project, I will investigate how these building parameters affect overall heating
and cooling loads of buildings by implementing a multi-layer perception with backward
propagation of errors. I will test what parameters, or combination of parameters, have the
greatest effect on a buildings overall heating and cooling loads, during operation.
References:
Tsanas, A. Xifara: 'Accurate quantitative estimation of energy performance of residential
buildings using statistical machine learning tools', Energy and Buildings, Vol. 49, pp. 560-567,
2012
Lee, S., Park, Y., and Kim, C. (2012) Investigating the Set of Parameters Influencing Building
Energy Consumption. ICSDC 2011: pp. 211-221.