The ability to forecast energy prices in the long-term is important for a wide range of reasons, from the formulation of countries' energy and transportation policies to the defensive strategies of nations to investment decisions within the private sector. Despite the importance of these predictions, however, forecasters and market pundits face a difficult challenge when trying to forecast over the long-term. While statistical models can credibly rely on assumptions about the relationship between variables in the short-term, they are frequently less reliable in the long-term as political and technological transformations profoundly change how the economy works over time. Towards improving long-term predictions for energy commodities, this paper uses the elicitation and aggregation of experts' beliefs to put forward forecasts for crude oil and natural gas prices by incentivizing experts to tell the truth and minimising their own biases through the application of the Bayesian Truth Serum. With this approach, we generated both short-term and long-term forecasts, and used the short-term forecast to validate the quality of the experts' predictions.