Optimization and Prediction of Energy Consumption, Daylighting, and Thermal Comfort of Buildings in Tropical Areas

As awareness of the ecological environment and sustainable development has increased, green buildings have received significant attention in the design stage. For the initial design stage of buildings in the tropics, cooling energy consumption, daylighting, and thermal comfort are necessary steps for green and energy-saving design. Therefore, this study focuses on three objectives: (1) cooling load, (2) useful daylight illuminance (UDI), and (3) the predicted mean vote (PMV). First, this research uses Rhino3D and the Grasshopper plug-in to build an architectural model and uses the Octopus plug-in in Grasshopper to iteratively calculate the target value to solve the multiobjective balance problem and find the relative optimal value. Next, the optimized design value is compared with the initial solution, and the cooling energy consumption is reduced by 7.48%–7.76%, the UDI increases by 0.44%–2.07%, and the PMV is reduced by 25.67%–27.43%. It is shown that the optimized layout of the office achieves energy-saving optimization in energy consumption, daylighting, and thermal comfort. Finally, the backpropagation (BP) neural network established in this research is shown to achieve good prediction of the target value and achieves the goal of green energy-saving.

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