Given the large and growing volume of big data and frequent use of complex analytical queries, understanding energy efficiency of query processing has become a critical research issue, as highlighted by database systems papers in the last few years. Common software solutions mainly consider IO cost models to estimate energy consumption when executing queries. On the other hand, current hardware solutions benefit from advances in the development of green components and their associated tuning techniques, especially dynamic voltage and frequency scaling (DVFS), which can balance the performance and power consumption of multicore CPUs. Unfortunately, to the best of our knowledge, there is an absence of solutions mixing both (hardware and software). Heeding this gap, we propose a novel predictive model to measure and predict energy consumption of analytical queries when using multi-core processors and different frequency configurations. We first experimentally illustrate the surprising impact of CPU frequency and the number of processor cores on execution time, power and energy consumption. Second, we introduce an extended predictive model that enriches a well-known machine learning cost model with our new angle, the frequency scaling in multi-core environment. Specifically, by using Support Vector Regression and Random Forest Regression, we compute the energy coefficients of an accurate regression model for energy prediction. Experiments with benchmark data sets TPC-H and TPC-DS evaluate our proposed framework in terms of energy consumption reduction, showing promising results.