# Energy Predictive Models with Limited Data using Transfer Learning

arXiv: https://arxiv.org/abs/1906.02646 · HTML: https://mlsanity.com/paper/1906.02646/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Meta-Learning for Neural Network-based Temporal Point Processes](https://mlsanity.com/paper/2401.15846/) (2024)
- [Transfer Learning on Transformers for Building Energy Consumption Forecasting -- A Comparative Study](https://mlsanity.com/paper/2410.14107/) (2024)
- [Meta-Learning for Koopman Spectral Analysis with Short Time-series](https://mlsanity.com/paper/2102.04683/) (2021)
- [Representation Learning by Ranking across multiple tasks](https://mlsanity.com/paper/2103.15093/) (2021)
- [Neural forecasting at scale](https://mlsanity.com/paper/2109.09705/) (2021)
- [Performance of Deep Learning models with transfer learning for multiple-step-ahead forecasts in monthly time series](https://mlsanity.com/paper/2203.11196/) (2022)
- [CaberNet: Causal Representation Learning for Cross-Domain HVAC Energy Prediction](https://mlsanity.com/paper/2511.06634/) (2025)

## Abstract (arXiv)

In this paper, we consider the problem of developing predictive models with limited data for energy assets such as electricity loads, PV power generations, etc. We specifically investigate the cases where the amount of historical data is not sufficient to effectively train the prediction model. We first develop an energy predictive model based on convolutional neural network (CNN) which is well suited to capture the interaday, daily, and weekly cyclostationary patterns, trends and seasonalities in energy assets time series. A transfer learning strategy is then proposed to address the challenge of limited training data. We demonstrate our approach on a usecase of daily electricity demand forecasting. we show practicing the transfer learning strategy on the CNN model results in significant improvement to existing forecasting methods.