1 research outputs found
Using an Ancillary Neural Network to Capture Weekends and Holidays in an Adjoint Neural Network Architecture for Intelligent Building Management
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.Comment: 9 pages, 11 figures, 2 table