This paper presents the development of an artificial intelligent water demand forecasting model. The model comprises a single hidden-layer feed-forward neural network trained in using a differential evolution algorithm. Multiple feature selection techniques were employed to identify the minimal subset of features for optimal learning, namely Pearson correlation, information gain, symmetrical uncertainty, Relief-F attribute and principal component analysis. The performance of the feature selection techniques was compared to a baseline scenario comprising a full set of data covering potential casual variables including weather, socioeconomic and historical water consumption data. The performance of the models was evaluated based on accuracy. Results show that the five feature selection techniques outperformed the baseline scenario. More importantly, the subset of features obtained from the Pearson correlation technique produced the most superior model in terms of model accuracy. Findings from the study suggest that the inclusion of weather and socioeconomic variables in water demand modelling could enhance the accuracy of forecasts and cater for the impacts of climate and socioeconomic variations in water demand planning and management.