Day Ahead Pricing Forecasts for Short Term Scheduling in Power Markets — A Deep Learning based approach

Written by Akshit Gupta and Cremers Sho

In this blog, we will go over deep learning-based RNNs (specifically LSTMs) to forecast day-ahead electricity prices in the context of power markets. The work is mainly based on the approach highlighted in [1] and uses publicly available real-world datasets for weather and electricity prices for training and evaluation. Our results show that RNNs (bidirectional LSTMs) are a powerful tool for forecasting electrical prices with quantifiable uncertainties. In doing so, we were successfully able to replicate the results of [1], albeit on a different dataset.

All our code is open source and available on Github here.

Motivation (The who, what and why?)

In the modern world, electricity is a tradable quantity. The central authority regulates power grids in most countries to ensure transparency in the power markets. Since electricity storage is still costly, a balance between supply (generation at source) and demand (consumption at the sink) is desired in power grids. Due to these constraints, electricity generators and consumers (hereon, called energy actors as per [1]) submit pricing bids to the central authority at a fixed particular time of the day based on their multi-step ahead probabilistic forecasts to maximize their return on investment. These electricity prices are dependent on a large number of…