An Approach for Supporting Temporary Power Technology Decisions in Construction Using Energy Forecasting

As construction sites shift toward electrification to meet net-zero climate goals, their dependence on grid-supplied electricity increases significantly. However, this transition is challenged by growing grid congestion across the Netherlands, limiting the availability of reliable and sufficient power. This thesis presents a structured approach that forecasts energy demand on construction sites and supports the selection of supplemental power technologies when grid capacity falls short. The approach integrates real-world equipment data, project planning models, and decision-support tools, including the Analytic Hierarchy Process (AHP), decision matrix, and decision trees. These models are evaluated for their ability to assist construction managers in selecting the most suitable temporary power setups. Expert feedback indicated that decision trees are the most intuitive and user-friendly, while AHP offers depth but is more complex to apply. Overall, the results demonstrate that combining forecasting with structured decision support can improve energy planning on construction sites.