Reliable models that can forecast energy demand (G) are needed to implement affordable
and sustainable energy systems that promote energy security. In particular, accurate G
models are required to monitor and forecast local electricity demand. However, G
forecasting is a multivariate problem, and thus models must employ robust pattern
recognition algorithms that can detect subtle variations in G due to causal factors, such as
climate variables. Therefore, this study developed an artificial neural network (ANN) model …

Regression Tree (RT) forecasting models are widely used in short-term demand forecasting.
Likewise, Self-Organizing Maps (SOM) models are known for their ability to cluster and
organize unlabeled big data. Herein, a combination of these two Machine Learning (ML)
techniques is proposed and compared to a standalone RT and a Seasonal Autoregressive
Integrated Moving Average (SARIMA) models, in forecasting the short-term water demand of
a municipality. The inclusion of the Unsupervised Machine Learning clustering model has …