Heating load and cooling forecasting are essential for estimating energy consumption, and consequently, helping engineers in improving the energy performance right from the design phase of buildings. The capacity of heating ventilation and air-conditioning system of the building contributes to the operation cost. Moreover, building being one of the sectors with heavy energy use, it is required to develop an accurate model for energy forecasting of building and constructing energy-efficient buildings. This paper explores different machine learning techniques for predicting the heating load and cooling load of residential buildings. Among these methods, we focus on advanced techniques like Multivariate Adaptive Regression Splines (MARS), Extreme Learning Machine (ELM) and a hybrid model of MARS and ELM along with a comparison of the results with those of more conventional methods like linear regression, neural network, Gaussian processes and Radial Basis Function Network. The MARS model is a non-parametric regression model that splits the data and fits each interval into a basis function and ELM is similar to a Single Layer Feed-forward Neural Network except that in ELM randomly assigned input weights are not updated. As an improvement, we have tried a hybrid model that uses MARS to evaluate the importance of every parameter in the prediction and these important parameters have been fed to the ELM to build hybrid model and it can be seen that this boosts the ELM performance to match up to the accuracy of MARS with lesser computation time. Finally, a comparative study examines the performances of the different techniques by measuring different performance metrics. © 2017 Elsevier Ltd