2023 article
A Modified Sequence-to-point HVAC Load Disaggregation Algorithm
2023 IEEE POWER & ENERGY SOCIETY GENERAL MEETING, PESGM.
author keywords: demand response; HVAC load; load disaggregation; machine learning; convolutional neural network (CNN); transfer learning
TL;DR:
Simulation results show that the proposed modified S2P algorithm outperforms the original S1P model and the support-vector machine based approach in accuracy, adaptability, and transferability.
(via Semantic Scholar)
open access via arxiv.org (repository)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(Web of Science; OpenAlex)
Source: Web Of Science
Added: November 20, 2023
This paper presents a modified sequence-to-point (S2P) algorithm for disaggregating the heat, ventilation, and air conditioning (HVAC) load from the total building electricity consumption. The original S2P model is convolutional neural network (CNN) based, which uses load profiles as inputs. We propose three modifications. First, the input convolution layer is changed from 1D to 2D so that normalized temperature profiles are also used as inputs to the S2P model. Second, a drop-out layer is added to improve adaptability and generalizability so that the model trained in one area can be transferred to other geographical areas without labelled HVAC data. Third, a fine-tuning process is proposed for areas with a small amount of labelled HVAC data so that the pre-trained S2P model can be fine-tuned to achieve higher disaggregation accuracy (i.e., better transferability) in other areas. The model is first trained and tested using smart meter and sub-metered HVAC data collected in Austin, Texas. Then, the trained model is tested on two other areas: Boulder, Colorado and San Diego, California. Simulation results show that the proposed modified S2P algorithm outperforms the original S2P model and the support-vector machine based approach in accuracy, adaptability, and transferability.