The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli–Turkey

Turkey does not have petrol and natural gas reserves on a large scale. National energy resources are lignite and hydropower. Together with increasing environmental problems and diminishing fossil resources, studies focusing on energy reduction as well as usage of renewable energy resources have accelerated. However, taking the technological and economical impossibilities into account, the most logical solution is energy saving by providing energy efficiency in households. In this study, an artificial neural network (ANN) model is developed in order to predict hourly heating energy consumption of a model house designed in Denizli which is located in Central Aegean Region of Turkey. Hourly heating energy consumption of the model house is calculated by degree-hour method. ANN model is trained with heating energy consumption values of years 2004–2007 and tested with heating energy consumption values of year 2008. The training and test figures were depicted for February month of these years. Best estimate is found with 29 neurons and a good coherence is observed between calculated and predicted values. According to the results obtained, root-mean-squared error (RMSE), absolute fraction (R2) and mean absolute percentage error (MAPE) values are 1.2575, 0.9907, and 0.2091 for training phase and 1.2125, 0.9880, and 0.2081 for testing phase respectively.