Computer Science > Machine Learning
[Submitted on 20 Oct 2026]
Title:Extreme Low-State Quantized Transformer for Electricity-Load Forecasting
View PDF HTML (experimental)Abstract:Aggressive low-state weight quantization can substantially reduce neural-network representational flexibility, making predictive performance difficult to preserve when only a few discrete weight states are available. We investigate this regime for Transformer-based electricity-load forecasting using 3-, 5-, 7-, and 16-state weight quantization across feed-forward-only and attention-plus-feed-forward quantization. Two tasks predict either the next hour or the next 24 hours from the preceding 48 hours across two co-primary electricity-consumption datasets. Direct post-training quantization (PTQ) increased mean normalized root-mean-square error (NRMSE) relative to full precision by 6.25 and 7.04 percentage points in Datasets A and B, whereas quantization-aware fine-tuning (QAT-FT) recovered approximately 90.2% and 94.5% of these penalties. Quantization-aware training from random initialization (Scratch-QAT) also produced competitive low-state solutions, with particularly favorable behavior at three states. We further introduce accumulated-update thresholding, which accumulates optimizer-proposed weight changes before commitment. Relative to paired Scratch-QAT controls, this reduced committed shadow-weight updates by approximately 99.7% and discrete state transitions by approximately 77--80%, while mean NRMSE changed by only +0.042 and +0.197 percentage points. These results show that useful Transformer forecasting can be retained under highly discrete weight representations while strongly suppressing target-state activity, supporting a prospective route toward compact, low-activity edge forecasting using multilevel synaptic devices.
From: Jayasimha Atulasimha [view email][v1] Tue, 20 Oct 2026 18:15:05 UTC (272 KB)
Loading...
Bibliographic and Citation Tools
Bibliographic Explorer (What is the Explorer?)
Connected Papers (What is Connected Papers?)
Litmaps (What is Litmaps?)
scite Smart Citations (What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv (What is alphaXiv?)
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub (What is DagsHub?)
Gotit.pub (What is GotitPub?)
Hugging Face (What is Huggingface?)
ScienceCast (What is ScienceCast?)
Recommenders and Search Tools
Influence Flower (What are Influence Flowers?)
CORE Recommender (What is CORE?)
(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.