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The US EIA estimated in 2017 about 39\% of total U.S. energy consumption wasby the residential and commercial sectors. Therefore, Intelligent BuildingManagement (IBM) solutions that minimize consumption while maintaining tenantcomfort are an important component in addressing climate change. A forecastingcapability for accurate prediction of indoor temperatures in a planning horizonof 24 hours is essential to IBM. It should predict the indoor temperature inboth short-term (e.g. 15 minutes) and long-term (e.g. 24 hours) periodsaccurately including weekends, major holidays, and minor holidays. Otherrequirements include the ability to predict the maximum and the minimum indoortemperatures precisely and provide the confidence for each prediction. Toachieve these requirements, we propose a novel adjoint neural networkarchitecture for time series prediction that uses an ancillary neural networkto capture weekend and holiday information. We studied four long short-termmemory (LSTM) based time series prediction networks within this architecture.We observed that the ancillary neural network helps to improve the predictionaccuracy, the maximum and the minimum temperature prediction and modelreliability for all networks tested.

Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management