Forecasts of distributed energy resource deployment are becoming increasingly important in electric power purchase plans and difficult for countries with limited data. This study utilizes the Customer Adoption Model to forecast the deployment of behind-the-meter distributed solar photovoltaics and battery energy storage systems until the year 2050 and Thailand is used as a case study of the countries with limited data. Comparing methods and results from this study with those used in past studies shows that methodological choices can produce diverging results that shape investment plans and the estimated cost of power supplies. Several input variables of the Customer Adoption Model are discussed that will require continuous refinements as more data become available. The results show that pairing solar systems with batteries could in principle accelerate solar deployment and carbon emissions reduction but the high cost of batteries lengthens the payback period, raising questions about forecasting methodologies that rely mainly on the payback period. The methodological contribution points to a "chicken-and-egg" problem between forecasting and policy uncertainties: accurate forecasting depends on policy certainty, but getting policy right depends on accurate forecasting. Integrated scenario construction and the determination of a specific timeframe for achieving the adoption goal can help policymakers understand the impacts of different policy designs on distributed energy resource deployment and overcome this problem.