# Hybrid Renewable Systems for Small Energy Communities: What Is the Best Solution?
## 1 Introduction  
Water is a vital resource, fundamental not only for various human activities but also for maintaining life-sustaining processes [1]. The growing water demand, combined with declining freshwater resources, necessitates vigilant monitoring of water consumption to effectively manage the increasing pressure on these resources [2]. To mitigate the effects of human activities, integrating artificial intelligence into water distribution systems is transforming urban areas' efficiency, sustainability, and resilience [3]. Artificial intelligence techniques in conjunction with big data analytics enhance the precision of simulation models by reducing inaccuracies, thereby refining model feedback [4]. These techniques are expected to improve the efficiency and resilience of hydraulic systems while also contributing to the sustainable management of global water systems. They facilitate the reduction of pressure [5], assessment of distribution flows [6], and mitigation of leaks through the use of pressure-reducing valves or pipe replacement [7]. Such advancements significantly improve system performance, with performance increases ranging from 36% to 65% [8]. These improvements stem from several interrelated factors, including increasedprocessing speed, better data handling, and more accurate predictions made possible by leveraging cutting-edge technologies. The gains in performance help to enhance system reliability, minimize downtime, and optimize the use of existing resources, leading to substantial benefits for industries that rely on complex infrastructures, such as water distribution and energy management. Research shows that advanced algorithms, digital twin optimization models, and machine learning techniques play a crucial role in optimizing water resource management [9]. Advanced algorithms streamline various water management processes, from optimizing pump operations and distribution schedules to predicting system failures and identifying anomalies in real time. Digital twin optimization models, which create virtual replicas of physical systems, allow for testing and scenario analysis under different conditions without impacting the actual infrastructure. This capability helps operators to dynamically optimize the network and improve resilience against disruptions. Machine learning techniques, meanwhile, are used for pattern recognition and predictive analytics, learning from historical data to enhance future performance. When combined, these technologies can create a more integrated and adaptive approach to managing water resources, reducing inefficiencies, and addressing emerging challenges in water distribution. Moreover, these advancements facilitate accurate forecasting of demand, which is crucial for ensuring the reliability and sustainability of water supply systems [10]. Predictive models help utilities forecast water consumption patterns based on factors like population growth, seasonal changes, and socio-economic activities. Such demand predictions enable more efficient planning and allocation of resources, reducing both operational costs and the environmental footprint of water management practices. Detecting water and energy losses or infrastructure failures at an early stage is another significant advantage [11]. By using data analytics and sensor networks, predictive maintenance techniques can pinpoint potential issues before they escalate, such as leaks in pipes or electrical faults in pump stations. Early detection of such problems minimizes repair costs, reduces water wastage, and avoids major service disruptions. This capability also contributes to prolonging the lifespan of equipment, as systems can be maintained proactively based on real-time insights. These predictive approaches enable water managers to make real-time decisions [12], such as adjusting water distribution routes or changing pumping schedules based on current demand and supply conditions.