In an institutional building, energy consumption can be predicted by Long Short-Term Memory (LSTM). There has been a case where weather data had forecasted a novel energy consumption prediction for a futuristic value for energy consumption. Data was forecasted by the Malaysian Meteorological Department (MET). Further, there were results and a proposed approach which showed that energy policies could be implemented much faster as there were accurate assumptions of energy consumption based on diagnosis and fault detection on buildings.The study concluded that the forecast of energy consumption of an institutional building can be predicted from an LSTM model, which gives a daily day-ahead forecast. The day, week, weekend or holiday affects the LSTM model. Further, to improve the accuracy of the model, environmental external variables are needed such as wind speed, solar radiation, temperature and so forth.