Developing an Intelligent Decision Support System for large-scale smart grid communication network planning

The placement of routers and gateways in wireless communication networks for Smart Grid Advanced Metering Infrastructure (AMI) is a complex problem often addressed in the literature through heuristics, clustering, or linear programming approaches. In large-scale scenarios, the complexity is directly related to the region's number of smart meters and poles. The terrain profile can introduce signal quality degradation between a smart meter and the routers and gateways, further complicating the problem. This study presents the AIDAML method, representing a preliminary step toward developing an Intelligent Decision Support System (IDSS) for an effective positioning strategy. AIDA-ML incorporates a feature engineering-based strategy and utilizes machine learning algorithms to learn from the results computed by a heuristic method called AIDA. The heuristic approach is too time-consuming, relying on external resources like numerous terrain profile API calls and a deep understanding of wireless communication loss models. To implement a machine learning-based and more straightforward approach, a feature engineering process is employed to capture the operational characteristics and results of the analytical method while requiring less processing time. Through experiments conducted with real-world data from 26 cities in the state of Parana, Brazil, comprising 466,237 smart meters and 352,867 poles, the results obtained using machine learning algorithms suggest that AIDA-ML can ensure the connection coverage of smart meters while meeting the same minimum requirements established for the analytical method. Moreover, AIDA-ML offers the advantage of reducing the processing time by 87.60% and by 96.86% the overall search space size compared to the heuristic approach.