Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management
This work addresses energy consumption reduction in residential and commercial sectors, which account for 39% of U.S. energy use, by enhancing forecasting for building management, though it appears incremental as it builds on existing LSTM methods with a specific architectural tweak.
The authors tackled the problem of accurately forecasting indoor temperatures for intelligent building management by proposing a novel adjoint neural network architecture with an ancillary neural network to capture weekend and holiday effects, resulting in improved prediction accuracy, maximum/minimum temperature prediction, and model reliability across four tested LSTM-based networks.
The US EIA estimated in 2017 about 39\% of total U.S. energy consumption was by the residential and commercial sectors. Therefore, Intelligent Building Management (IBM) solutions that minimize consumption while maintaining tenant comfort are an important component in addressing climate change. A forecasting capability for accurate prediction of indoor temperatures in a planning horizon of 24 hours is essential to IBM. It should predict the indoor temperature in both short-term (e.g. 15 minutes) and long-term (e.g. 24 hours) periods accurately including weekends, major holidays, and minor holidays. Other requirements include the ability to predict the maximum and the minimum indoor temperatures precisely and provide the confidence for each prediction. To achieve these requirements, we propose a novel adjoint neural network architecture for time series prediction that uses an ancillary neural network to capture weekend and holiday information. We studied four long short-term memory (LSTM) based time series prediction networks within this architecture. We observed that the ancillary neural network helps to improve the prediction accuracy, the maximum and the minimum temperature prediction and model reliability for all networks tested.