Energy consumption in the building sector is about 40% of total energy consumed
globally and is trending upwards, along with its contribution to greenhouse gas
(GHG) emissions. Given the adverse impacts of GHG emissions, it is crucial to
integrate energy efficiency into building designs. The most significant opportunities
for enhancing energy performance are present during the initial phases of building
design, when there is less impact of other design constraints. Various tools exist
for simulating different design options and providing feedback in terms of energy
consumption and comfort parameters. These simulation outputs must then be
analyzed to derive design solutions. This paper presents an innovative approach
that utilizes user input parameters, processes them through cloud computing,
and outputs easily understandable strategies for energy-efficient building design.
The methodology employs Asynchronous Distributed Task Queues (DTQ) - a more
scalable and reliable alternative to conventional speedup techniques-for conducting
parametric energy simulations in the cloud. The goal of this approach is to assist
design teams in identifying, visualizing, and prioritizing energy-saving design
strategies from a range of possible solutions for each project. Furthermore, a tool
‘eDOT’ has been developed utilizing the discussed methodology. Unlike existing tools,
eDOT leverages artificial intelligence to dynamically generate and provide design
strategies during the early phases of design process. By simplifying the simulation
process, eDOT enables design teams to make informed, data-driven decisions
without needing to interpret complex simulation outputs. A case study simulated
for two locations is provided in this paper to demonstrate the effectiveness of eDOT,
further underscoring its practical impact on energy-efficient building design.