# Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model

arXiv: https://arxiv.org/abs/2112.13681 · HTML: https://mlsanity.com/paper/2112.13681/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 38 references that are in the map.
- First posted on arXiv 23 Dec 2021.

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

- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [Forecasting day-ahead electricity prices in Europe: the importance of considering market integration](https://mlsanity.com/paper/1708.07061/) (2017)
- [Learning The Sequential Temporal Information with Recurrent Neural Networks](https://mlsanity.com/paper/1807.02857/) (2018)

## Built on by (most cited first, 8 of 8)

- [Reduced Optimal Power Flow Using Graph Neural Network](https://mlsanity.com/paper/2206.13591/) (2022)
- [Feasibility Layer Aided Machine Learning Approach for Day-Ahead Operations](https://mlsanity.com/paper/2208.06742/) (2022)
- [Spatio-Temporal Deep Learning-Assisted Reduced Security-Constrained Unit Commitment](https://mlsanity.com/paper/2306.01570/) (2023)
- [Graph Neural Network-based Power Flow Model](https://mlsanity.com/paper/2307.02049/) (2023)
- [N-1 Reduced Optimal Power Flow Using Augmented Hierarchical Graph Neural Network](https://mlsanity.com/paper/2402.06226/) (2024)
- [Analysis of Weather and Time Features in Machine Learning-aided ERCOT Load Forecasting](https://mlsanity.com/paper/2310.08793/) (2023)
- [Linearization of ReLU Activation Function for Neural Network-Embedded Optimization: Optimal Day-Ahead Energy Scheduling](https://mlsanity.com/paper/2310.01758/) (2023)
- [Graph Neural Network-Accelerated Network-Reconfigured Optimal Power Flow](https://mlsanity.com/paper/2410.17460/) (2024)

## Abstract (arXiv)

Electricity price is a key factor affecting the decision-making for all market participants. Accurate forecasting of electricity prices is very important and is also very challenging since electricity price is highly volatile due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and non-linear behavior within the input data. We have used ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model is accurate and efficient in electricity price forecasting as compared to the support vector machine (SVM) model, fully-connected neural network model, LSTM model and the LRCN model without the conditional error correction stage.