# Concepts for Automated Machine Learning in Smart Grid Applications

arXiv: https://arxiv.org/abs/2110.13585 · HTML: https://mlsanity.com/paper/2110.13585/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 46 references that are in the map.
- First posted on arXiv 26 Oct 2021.

## Builds on (most cited first, 5 of 5)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Adaptation Strategies for Automated Machine Learning on Evolving Data](https://mlsanity.com/paper/2006.06480/) (2020)
- [pyWATTS: Python Workflow Automation Tool for Time Series](https://mlsanity.com/paper/2106.10157/) (2021)
- [Data-Driven Copy-Paste Imputation for Energy Time Series](https://mlsanity.com/paper/2101.01423/) (2021)
- [Integrating Battery Aging in the Optimization for Bidirectional Charging of Electric Vehicles](https://mlsanity.com/paper/2009.12201/) (2020)

## Built on by (most cited first, 4 of 4)

- [Review of automated time series forecasting pipelines](https://mlsanity.com/paper/2202.01712/) (2022)
- [MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis](https://mlsanity.com/paper/2309.15521/) (2023)
- [AutoPV: Automated photovoltaic forecasts with limited information using an ensemble of pre-trained models](https://mlsanity.com/paper/2212.06797/) (2022)
- [On autoregressive deep learning models for day-ahead wind power forecasting with irregular shutdowns due to redispatching](https://mlsanity.com/paper/2412.00423/) (2024)

## Abstract (arXiv)

Undoubtedly, the increase of available data and competitive machine learning algorithms has boosted the popularity of data-driven modeling in energy systems. Applications are forecasts for renewable energy generation and energy consumption. Forecasts are elementary for sector coupling, where energy-consuming sectors are interconnected with the power-generating sector to address electricity storage challenges by adding flexibility to the power system. However, the large-scale application of machine learning methods in energy systems is impaired by the need for expert knowledge, which covers machine learning expertise and a profound understanding of the application's process. The process knowledge is required for the problem formalization, as well as the model validation and application. The machine learning skills include the processing steps of i) data pre-processing, ii) feature engineering, extraction, and selection, iii) algorithm selection, iv) hyperparameter optimization, and possibly v) post-processing of the model's output. Tailoring a model for a particular application requires selecting the data, designing various candidate models and organizing the data flow between the processing steps, selecting the most suitable model, and monitoring the model during operation - an iterative and time-consuming procedure. Automated design and operation of machine learning aim to reduce the human effort to address the increasing demand for data-driven models. We define five levels of automation for forecasting in alignment with the SAE standard for autonomous vehicles, where manual design and application reflect Automation level 0.