1. A method of performing analysis of energy consumption for a portfolio of buildings using multi-step statistical modeling, comprising: receiving energy consumption data associated with a building; receiving building characteristic data associated with the building; receiving building operation and activities data associated with the building; receiving weather data; fitting said energy consumption data, said building characteristic data, said building operation and activities data, and said weather data to generate a variable based degree day model; determining, by a computer processor, a base load factor, a heating coefficient and a cooling coefficient associated with the building and an error term from the variable based degree day model; generating a plurality of multivariate regression models based on the base load factor, the heating coefficient, the cooling coefficient and the building characteristic data; generating a time series model for the error term to model seasonal factors which reflect monthly dependence on energy use and an auto-regressive integrated moving average model (ARIMA) which reflects temporal dependent patterns of the energy use; generating, by the computer processor, a performance score associated with energy consumption for base load of the building, a performance score associated with energy consumption for cooling the building, or a performance score associated with energy consumption for heating the building, or combinations thereof, based on the generated variable based degree day model and the multivariate regression models, wherein the variable based degree day model includes 
 y it =b i +c i ( T t −T i 0 ) + +h i ( T i 0 −T t ) + +ε it , wherein i is building index; t is time index; y it is energy consumption for building i at time t; b i is building effect for base load; T t is outside temperature and T t 0 is building specific base temperature associated with building i; c i is building effects for cooling coefficient; h i is building effects for heating coefficient; and ε it is an error term that represents unaccountable energy consumption.