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

arXiv: https://arxiv.org/abs/1902.06778 · HTML: https://mlsanity.com/paper/1902.06778/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Dec 2018.

## Built on by (most cited first, 2 of 2)

- [Enhance Image-to-Image Generation with LLaVA-generated Prompts](https://mlsanity.com/paper/2406.01956/) (2024)
- [Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models](https://mlsanity.com/paper/2407.16165/) (2024)

## Abstract (arXiv)

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.