Wholesale Electricity Price Forecasting Using Integrated Long-Term Recurrent Convolutional Network Model

Abstract

Electricity price forecasts have become a fundamental factor affecting the decision-making of all market participants. Extreme price volatility has forced market participants to hedge against volume risks and price movements. Hence, getting an accurate price forecast from a few hours to a few days ahead is very important and very challenging due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority of contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of a 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 nonlinear behavior within the input data. ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region have been used 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 shows the highest accuracy and efficiency in electricity price forecasting as compared to the support vector machine (SVM) model, fully connected neural network model, LSTM model, and the traditional LRCN model without the conditional error correction stage.

Current Challenges in Operation, Performance, and Maintenance of Photovoltaic Panels

“…All of these factors are suitable for prediction with artificial intelligence algorithms. For PV generation, Section 4.1 provides a summary, while price prediction has its own extended literature, e.g., with ensemble learning methods [172], long and short-term memory networks with the sparrow search algorithm [173], transfer learning [174], and integrated long-term recurrent convolutional networks [175]. Several comparative studies are also available inspecting multiple machine learning models, e.g., for applications in Spain [176] and Iran [177].…”

Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

“…Having an accurate prediction of electricity prices is quite important for various entities, including market participants, consumers, renewable energy integration, grid operators, policy-makers, investors, and energy efficiency initiatives [10][11][12]. It helps to facilitate informed decision-making, risk management, cost savings, grid stability, and the transition to a more sustainable and efficient energy system [9,13].…”

“…In [26], the long short-term memory (LSTM) network is indicated to be a powerful model in financial time series like stock prices. LSTM, a deep learning model, is also advantageous for EPF due to its ability to effectively capture temporal dependencies, learn from sequential data, handle complex patterns, and adapt to different forecasting horizons [4,5,10,11,13,21,23,[27][28][29][30][31].…”

Electricity price forecasting model based on MLP-mixer

“…The change of electricity price will directly affect the behaviors of individual users, distributed energy resource aggregators and local microgrid operators with the goal of maximizing profit or minimizing cost, thus affecting their electricity consumption status and the amount of electricity sold to or purchased from the grid. Accurate electricity price prediction is crucial for power companies and other market participants to make more informed decisions in a competitive and changeable environment, such as demand scheduling and power scheduling, which can also improve the reliability of the power system to a certain extent 2 . Various statistical models, such as vector autoregressive (VAR) and wavelet transform 3 , are often used in traditional electricity price forecasting.…”