Baseline energy modeling using artificial neural network - cross validation technique / Wan Nazirah Wan Md Adnan, Nofri Yenita Dahlan and Ismail Musirin

This paper presents a baseline energy model development using artificial neural networks (ANN) with CrossValidation (CV) technique for a small dataset. The CV technique is used to examine generalization abilities and model reliability of a small data. This CV-ANN model is simulated with thirty different structures using two CV techniques, Random Sampling Cross Validation (RSCV) and K-Fold Cross Validation (KFCV). Working days, class days and Cooling Degree Days (CDD) are used as ANN input meanwhile the ANN output is monthly electricity consumption. The coefficient of correlation (R) is used as performance function to check the model accuracy. The results are compared and best CV-ANN structure with the highest value of R is selected to develop the baseline energy model. The comparison reveals that most of the average R values are above 0.8 and it shows that the CV-ANN is capable to train the network even with small set of data. ANN-KFCV model with 6 neurons in hidden layer is chosen as the best model with average R is 0.91.