---
title: Regression Learners for Load Prediction
url: https://www.ml-quant.com/papers/ssrn/4893594/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4893594
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4893594
featured: 2024-07-17
citations: unknown
topic: Econometrics & Forecasting
---


# Regression Learners for Load Prediction

The research develops an electrical load forecasting system using machine learning, based on three years of data from the Wavi substation in India.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4893594
- Identifier: SSRN 4893594
- Released: 2024-07-13
- First featured: Quant Letter No. 57 (2024-07-17): https://www.ml-quant.com/issues/2024-07-17/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Econometrics & Forecasting

## Related

- [TKAN: Temporal Kolmogorov-Arnold Networks](https://www.ml-quant.com/papers/ssrn/4825654/): The article presents Temporal Kolomogorov-Arnold Networks (TKANs), a new neural network design that merges the benefits of Recurrent Neural Networks and Long Short-Term Memory for improved multistep time series forecasting.
- [Robust agents learn causal world models](https://www.ml-quant.com/papers/arxiv/2402.10877/): The research suggests that intelligent agents must learn an approximate causal model to generalize to new domains, impacting fields like transfer learning and causal inference.
- [Cost-aware Bayesian optimization via the Pandora's Box Gittins index](https://www.ml-quant.com/papers/arxiv/2406.20062/): The paper suggests using the Gittins index from the Pandora's Box problem in economics as a function for cost-aware Bayesian optimization.
- [Peer-induced Fairness: A Causal Approach for Algorithmic Fairness Auditing](https://www.ml-quant.com/papers/arxiv/2408.02558/): The study introduces peer-induced fairness, a new framework for auditing algorithmic fairness, differentiating between adverse outcomes due to algorithmic bias and individual shortcomings, and offering understandable feedback for those impacted by unfavorable decisions.
- [Universal randomised signatures for generative time series modelling](https://www.ml-quant.com/papers/arxiv/2406.10214/): The article presents a new model for analyzing financial data over time, using a unique Wasserstein-type distance method, and compares its performance with current standards.
- [Strong denoising of financial time-series](https://www.ml-quant.com/papers/arxiv/2408.05690/): The paper introduces a method to enhance the signal to noise ratio in financial data using auto-encoders, offering a new way to discover regularities in financial time-series.