Data Science for the Promotion of Sustainability in Smart Water Distribution Systems

In today's global landscape, the confluence of diminishing water reservoirs and the unprecedented surge in energy expenses represents a critical juncture in the discourse of sustainable resource management. In this context, Water Distribution Systems are changing their approach for the distribution of water to consumers. The sustainable management of water aligns with the Watergy concept, emphasiz-ing the fulfillment of user demand while optimizing the utilization of water and energy resources. Leveraging modern technologies such as Artificial Intelligence, Internet of Things, Big Data Analytics, the proactive forecasting of water and en-ergy demand stands as a fundamental strategy for achieving this objective. The significance of water demand forecasting extends beyond anticipating resource al-location for water distribution companies. It serves as a proactive measure, facili-tating the early detection of consumption irregularities and playing a crucial role in scheduling pumps. Conversely, energy consumption forecasting serves diverse functions, including energy optimization, anomaly identification in consumption patterns, and effective energy load planning. This study aims to pioneer short-term forecasting models for water demand and energy consumption within the water distribution sector, employing innovative machine learning-based methodologies. The utilization of global forecasting mod-els, the N-Beats machine learning algorithm, and transfer learning techniques demonstrates exceptional promise in creating robust predictive models for this domain, exhibiting notable performance.