Locational Marginal Price Forecasting Using SVR-Based Multi-Output Regression in Electricity Markets

Abstract

Electricity markets provide valuable data for regulators, operators, and investors. The use of machine learning methods for electricity market data could provide new insights about the market, and this information could be used for decision-making. This paper proposes a tool based on multi-output regression method using support vector machines (SVR) for LMP forecasting. The input corresponds to the active power load of each bus, in this case obtained through Monte Carlo simulations, in order to forecast LMPs. The LMPs provide market signals for investors and regulators. The results showed the high performance of the proposed model, since the average prediction error for fitting and testing datasets of the proposed method on the dataset was less than 1%. This provides insights into the application of machine learning method for electricity markets given the context of uncertainty and volatility for either real-time and ahead markets.

A Stochastic Decision-Making Tool Suite for Distributed Energy Resources Integration in Energy Markets

“…It provides an interval probabilistic forecast in the first stage. However, any multistep forecasting model such as those presented in [83] can be used for this task. In the second stage, Montecarlo simulations were used to construct multiple scenarios based on a normal probability density function (PDF) for each hour, guided by the forecast values, as well as upper and lower bounds provided by T2V-TE.…”

An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture

“…The electricity price is a crucial factor in energy markets and current power grids, playing a pivotal role in providing a reliable and economically efficient power supply [1,12,13]. Therefore, precise electricity price forecasting is essential for all stakeholders, as it empowers them to make informed decisions that increase profitability and reduce risks in competitive electricity markets.…”

“…This layer automates the feature engineering of these data and enhances their modeling capabilities. This data processing approach is based on the deployment presented in Equation (1).…”

“…The integration of distributed energy resources (DERs) into modern power systems is becoming increasingly important as new multi-resource electricity transactions emerge [1]. DERs, including residential photovoltaic (PV) systems, demand response (DR), and energy storage systems (ESSs), have the potential to provide numerous technical and environmental advantages [2].…”

Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

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