A Comparative Analysis of Automated Machine Learning Libraries for Electricity Price Forecasting

Abstract Reliable and accurate electricity price forecasting algorithms can be used to inform efficient energy consumption schedules and maximise profits for electricity traders. Operating within Ireland’s Integrated Single Electricity Market (I-SEM), traders can buy and sell electricity at fluctuating hourly rates whose day-ahead prices are published at approximately 13:00 GMT day-1. Access to electricity price predictions earlier than this publication time allows stakeholders an expanded timeframe to facilitate energy cost-aware scheduling. While many studies have been conducted to espouse various machine learning and statistical approaches to electricity price forecasting, these models tend to be bespoke and require in-depth knowledge regarding model implementation. The problem of requiring such expertise is not unique to time series forecasting, and research into mitigating such limitations exists in the form of Automated Machine Learning (AutoML). AutoML aims to derive effective models while automating various steps typically required for machine learning experimentation, such as pre-processing, model selection, validation, etc. Given the increasing proliferation of AutoML tools and frameworks, this paper applies eight Python-based AutoML libraries to day-ahead electricity price forecasting on an excerpt of I-SEM data. These libraries are compared across a series of error metrics and training times to produce an empirical benchmark that can be utilised to select high-performing AutoML tools for further price forecasting research and other forms of time series forecasting. AutoKeras is found to produce accurate forecasts but requires careful configuration to avoid long runtimes. PyCaret, Ludwig, FLAML and FEDOT also generate favourable results while being significantly easier to configure.