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Salah Vaisi

PhD Researcher
Dept. of Civil, Structural & Environmental Engineering



Modelling of seasonal heat demand

Keywords: CIBSE; DEC; University Campus; Heat map; energy modelling; energy efficiency; heat consumption.

CIBSE TM46 suggested a fixed-annual benchmark 240 kWh/m2 for college buildings under the category of “university campus” in 2008. Because the heat consumption depends radically on the ambient temperature, this fixed-annual benchmark is not very helpful. Additionally, the benchmark is not effective for predicting and evaluating of seasonal variable amounts of thermal energy demand since it does not deliver any difference between summer and winter figures.


In contrast, realizing a seasonal heat benchmark/model is crucial principally for managing of heat consumption at large levels such as urban or community scales. Therefore, the idea of seasonal heat demand modelling seems more helpful. In this PhD research, two approaches for generating monthly heat demand benchmarks/models for typical college buildings converting data of CIBSE benchmarks and Display Energy Certificates (DECs) are proposed.


The models rely upon composite benchmarking method instead of single use benchmarking usually found in the literature. For creating models five key factors are considered, including mixed use benchmark, revised CIBSE benchmark, activities & building area, heating degree days and typical operation hours of heating system.

The seasonal thermal models are used as a fundamental data to produce Heat Map (HM) for college buildings based on GIS technology. The HM can be used by energy suppliers, planners and managers for analysis, control, balance and sharing surplus heat energy at urban context.


Considering the future of renewable energy, particularly solar thermal HM plays a key role in energy efficiency/ management. In this regard, the findings of this research help for accurate and useful predicting the amount of heat demand in typical college buildings and generate seasonal thermal demand models and maps.


Project Supervisors: Associate Prof. Sarah & McCormack & Assistant Prof. Francesco Pilla