# Short-term Load Forecasting with Distributed Long Short-Term Memory

arXiv: https://arxiv.org/abs/2208.01147 · HTML: https://mlsanity.com/paper/2208.01147/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 28 references that are in the map.
- First posted on arXiv 1 Aug 2022.

## Builds on (most cited first, 3 of 3)

- [BayLIME: Bayesian Local Interpretable Model-Agnostic Explanations](https://mlsanity.com/paper/2012.03058/) (2020)
- [A Safety Framework for Critical Systems Utilising Deep Neural Networks](https://mlsanity.com/paper/2003.05311/) (2020)
- [Coverage Guided Testing for Recurrent Neural Networks](https://mlsanity.com/paper/1911.01952/) (2019)

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

- [Task-Aware Machine Unlearning and Its Application in Load Forecasting](https://mlsanity.com/paper/2308.14412/) (2023)
- [Private Knowledge Sharing in Distributed Learning: A Survey](https://mlsanity.com/paper/2402.06682/) (2024)

## Abstract (arXiv)

With the employment of smart meters, massive data on consumer behaviour can be collected by retailers. From the collected data, the retailers may obtain the household profile information and implement demand response. While retailers prefer to acquire a model as accurate as possible among different customers, there are two major challenges. First, different retailers in the retail market do not share their consumer's electricity consumption data as these data are regarded as their assets, which has led to the problem of data island. Second, the electricity load data are highly heterogeneous since different retailers may serve various consumers. To this end, a fully distributed short-term load forecasting framework based on a consensus algorithm and Long Short-Term Memory (LSTM) is proposed, which may protect the customer's privacy and satisfy the accurate load forecasting requirement. Specifically, a fully distributed learning framework is exploited for distributed training, and a consensus technique is applied to meet confidential privacy. Case studies show that the proposed method has comparable performance with centralised methods regarding the accuracy, but the proposed method shows advantages in training speed and data privacy.