A methodology to accurately forecast diurnal cooling load for institutional buildings is presented.

The forecasting model is developed using Artificial Neural Networks (ANNs).

The analysis is performed on cooling load data recorded over a period of two years.

The high variation in the load is reduced by introducing energy classes.

The developed ANN model can accurately forecast the cooling load classes for next 20 days.