Battle of Water Demand Forecasting: Integrating Machine Learning with a Heuristic Post-Process for Short-Term Prediction of Urban Water Demand

Abstract

The challenge in water demand forecasting within a Northeast Italy water distribution network (WDN) involves predicting demands across ten distinct District Metered Areas (DMAs) with varying characteristics and demand profiles. This is critical for optimizing system operation in the near future. The available data begins in January 2021, with unknown impacts of post-COVID socio-economic changes, like work-from-home policies. To address this, the team integrates heuristic and Machine Learning (ML) techniques to predict short-term demands and fill data gaps. A heuristic post-processing step, using weighted sums and historical trends, refines these predictions. This approach combines ML with traditional methods with a view to servicing developing nations.

Keywords

• forecasting
• statistical
• water demand
• weighted factors
• SARIMAX