Artificial Neural Network to optimize energy solutions of office buildings in subtropical monsoon climate

Abstract Reducing a building's energy use has many real-world applications. An early-stage design could have a quantitative foundation for energy-saving designs if energy consumption could be predicted quickly and accurately. The main issue that designers are currently dealing with is the incompatibility of building modelling and energy simulation software. In order to realize the flexibility of building energy systems, accurate and timely thermal load prediction for buildings is essential. Here, a model of an artificial neural network (ANN) is developed, for forecasting an office building's load demand and energy usage. A case study building was selected and analysed via Autodesk Revit and Green Building Studio. For the modelling of ANN, 438 simulated data samples were created based on different design parameters considering different window, wall and roof materials, and meteorological conditions considering dew point, dry bulb, wet bulb temperature and relative humidity of seven major cities in Bangladesh. The findings show that the artificial neural network (ANN) model has a high degree of precision in predicting annual electricity use and annual load demand. The coefficient of variation of the root mean squared errors corresponding to the predictions of load demand and electricity consumption is 0.132% and 0.105%, respectively. The model fits the data well, as evidenced by the R2 values of 0.99189 and 0.99505 for the load demand and electricity consumption predictions, respectively. The optimization results can subsequently lower the electricity consumption by 21.49%.