Predicting energy requirement for heating the building using artificial neural network

This paper explores total heat load and total carbon emissions of a six storey building by using artificial neural network (ANN). Parameters used for the calculation were conduction losses, ventilation losses, solar heat gain and internal gain. The standard back-propagation learning algorithm has been used in the network. The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. The calculated heat load was -1,211,228 kW per year. ANN application showed that data was best fit for the regression coefficient of 0.99812 with best validation performance of 1312.5203 in case of conduction losses.  Comparative analysis of carbon emission by different fuels has been followed by measures for reducing carbon emission.
Research Highlights:
•    Use of Artificial Neural Network to find heat load
•    Carbon emission calculation
•    Recommendations for renewable energy use
•    Regression coefficient calculation
•    Graphical representation of best validation performance