Prediction of optimal thermal insulation thickness using an artificial neural network

This study focused on optimising the thermal insulation of building walls in the climatic conditions of the Bukhara region. The energy consumption for heating was estimated based on the number of hot days, heating degree days, fuel price, heating system efficiency, wall thermal resistance, and insulation material properties. Hundreds of data points were generated using the analytical formula, which were then used to train an artificial neural network model. The model accurately predicted the optimal thickness and produced values close to those from the analytical calculations results. The predictions allow for quick assessment in practical conditions, providing additional convenience in selecting energy-saving solutions in construction. The study found that choosing the correct insulation thickness in colder climates results in a significant reduction in the building's annual energy demand and operating costs.

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