Predictive Optimization of Energy Consumption in Drinking Water Systems Using Parallel Algorithms

Optimising energy consumption has become a priority objective for companies and organisations, especially in essential services such as drinking water supply. Traditional strategies of maximising water storage generate energy inefficiency by forcing pumps to move large masses of water. These strategies also generate other negative effects, such as water loss in case of incidents and stress on aquifers. The problem addressed is the need to optimise the operation of drinking water production plants, seeking a balance between minimising stored water, guaranteeing supply and reducing energy consumption. Traditional optimisation systems have limitations in the number of variables they can handle and in the search for solutions in large search spaces. The optimisation problem is the search for solutions in an immense and computationally intractable data space. In this work, a new algorithm based on predictive artificial intelligence and the search for optimal values in distributed and parallel data spaces is proposed. This algorithm includes a consumption prediction module and a control module that calculates the optimal on/off sequence of the pumps. The algorithm uses a heuristic approach to define the search space and can be distributed and parallelised for computational feasibility. The prediction is based on a KNN algorithm that weights historical consumption, giving more weight to more recent data. To address the problem of large search spaces, the algorithm has been parallelised by distributing the computation of the optimisation matrix over several nodes. This allows the computation of local optima and the subsequent union of the results in a central node to obtain the global optimum. The proposed solution has been validated using real water infrastructure data in a city of approximately 5000 inhabitants in southeastern Spain. The validation shows that the new algorithm is more efficient than previous operational strategies, guaranteeing the service with a lower energy consumption of approximately 6%. The implementation of the algorithm has made it possible to maintain the water level at optimal levels, ensure the supply to the population, and adapt the operation to present and future needs.