A Comparative Modeling Framework for Forecasting Distributed Energy Resource Adoption Under Trend-Based and Goal-Oriented Scenarios

Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles (EVs), heat pumps (HPs), and rooftop photovoltaics (PVs), we evaluate four logistic-growth-based and two Bass-diffusion-based methods. Each method supports standard curve-fitting (trend-based) or incorporates explicit policy goals (goal-based), such as reaching a specified adoption threshold by a target year. An integrated flow diagram visually summarizes the decision process for method selection based on data availability, market maturity, and policy targets. Results show that Bass diffusion excels in early-stage or policy-driven markets like EVs, while logistic approaches perform better for PVs after subsidies are removed, with HP adoption falling in between. A key innovation is integrating future adoption targets into parameter estimation, enabling stakeholders to assess the required acceleration in adoption rates. The findings highlight the need to align model choice with data, market conditions, and policy objectives, offering practical guidance to accelerate DER deployment.