The increased penetration of distributed and volatile renewable generation requires the demand-side to be actively involved in energy balancing operations. This paper proposes a solution in which big data and machine learning methods are employed to enhance the capabilities of a Virtual Power Plant to participate and intelligently bid into a demand response energy market. The energy market being investigated consists of the frequency containment reserve market. First, we define the core decision-making required to overcome the uncertainties in the frequency containment reserve market participation for a Virtual Power Plant. Then, we focus on forecasting the frequency containment reserve prices for the day-ahead. We analyze the price data, and identify and collect the relevant features for the prediction of the prices. In addition, we select several regression analysis methods to be utilized for the prediction. Finally, we evaluate the performance of the implemented methods by executing several experiments, and compare the the performance with the performance of a state of the art autoregression method.