Electricity price forecasting has been a booming field over the years, with many methods and techniques being applied with different degrees of success. It is of great interest to the industry sector, becoming a must-have tool for risk management. Mo...
Energy markets with a high penetration of renewables are more likely to be challenged by price variations or volatility, which is partly due to the stochastic nature of renewable energy. The Danish electricity market (DK1) is a great example of such...
Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we...
In competitive power markets, electric utilities, power producers, and traders are exposed to increased risks caused by electricity price volatility. The growing reliance on renewable sources and their dependence on weather, nuclear uncertainty, mark...
The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply th...
This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task em...
This paper proposes a new hybrid model to forecast electricity market prices up to four days ahead. The components of the proposed model are combined in two dimensions. First, on the “vertical” dimension, long short-term memory (LSTM) neu...
In a modern and dynamic electricity market, ensuring reliable, sustainable and efficient electricity distribution is a pillar of primary importance for grid operation. The high penetration of renewable energy sources and the formation of competitive...
The electricity market is a complex, evolutionary, and dynamic environment. Forecasting electricity prices is an important issue for all electricity market participants. In this study, we shed light on how to improve electricity price forecasting acc...
This article presents original probabilistic price forecasting meta-models (PPFMCP models), by aggregation of competitive predictors, for day-ahead hourly probabilistic price forecasting. The best twenty predictors of the EEM2016 EPF competition are...
Competition in electricity markets leads to volatile conditions which cause persistent price fluctuations over time. This study explores the problem of electricity pricing fluctuations in the DE-LU bidding zone from October 2018 to March 2022 by appl...
Accurate electricity price forecasting plays an important role in the profits of electricity market participants and the healthy development of electricity market. However, the electricity price time series hold the characteristics of volatility and...
Forecasting the electricity price and load has been a critical area of concern for researchers over the last two decades. There has been a significant economic impact on producers and consumers. Various techniques and methods of forecasting have been...
In the context of the liberalization of electricity markets, forecasting prices is essential. With this aim, research has evolved to model the particularities of electricity prices. In particular, dynamic factor models have been quite successful in t...
The mainstream of EU policies is heading towards the conversion of the nowadays electricity consumer into the future electricity prosumer (producer and consumer) in markets in which the production of electricity will be more local, renewable and econ...
Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting elect...
Accurate electricity price forecasting has become a substantial requirement since the liberalization of the electricity markets. Due to the challenging nature of electricity prices, which includes high volatility, sharp price spikes and seasonality,...
Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special c...
This paper presents the analysis of the importance of a set of explanatory (input) variables for the day-ahead price forecast in the Iberian Electricity Market (MIBEL). The available input variables include extensive hourly time series records of wea...
Forecasting hourly spot prices for real-time electricity markets is a key activity in economic and energy trading operations. This paper proposes a novel two-stage approach that uses a combination of Auto-Regressive Integrated Moving Average (ARIMA)...
Electricity price forecasting has a paramount effect on generation companies (GenCos) due to the scheduling of the electricity generation scheme according to electricity price forecasts. Inaccurate electricity price forecasts could cause important lo...
Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990...
Electricity price forecasting plays a vital role in the financial markets. This paper proposes a self-adaptive, decomposed, heterogeneous, and ensemble learning model for short-term electricity price forecasting one, two, and three-months-ahead in th...
Electricity price forecasting is of significant practical importance, and improving prediction accuracy has become a key area of focus. Although substantial progress has been made in electricity price forecasting research, the unique characteristics...
This paper presents novel intraday session models for price forecasts (ISMPF models) for hourly price forecasting in the six intraday sessions of the Iberian electricity market (MIBEL) and the analysis of mean absolute percentage errors (MAPEs) obtai...
Deregulated electricity markets encourage firms to compete, making the development of renewable energy easier. An ordinary parameter of electricity markets is the electricity market price, mainly the day-ahead electricity market price. This paper des...
In the context of the electricity sector’s liberalization and deregulation, the accurate forecasting of electricity prices has emerged as a crucial strategy for market participants and operators to minimize costs and maximize profits. However,...
In the last three decades the vast majority of electricity price forecasting (EPF) research has concerned day-ahead markets. However, the rapid expansion of renewable generation—mostly wind and solar—have shifted the focus to intraday mar...
We conduct an extensive empirical study on the selection of calibration windows for day-ahead electricity price forecasting, which involves six year-long datasets from three major power markets and four autoregressive expert models fitted either to r...
The forecasting literature on intraday electricity markets is scarce and restricted to the analysis of volume-weighted average prices. These only admit a highly aggregated representation of the market. Instead, we propose to forecast the entire volum...
In the electricity market environment, the market clearing price has strong volatility, periodicity and randomness, which makes it more difficult to select the input features of artificial neural network forecasting. Although the traditional back pro...
Recently, the development in combining point forecasts of electricity prices obtained with different length of calibration windows have provided an extremely efficient and simple tool for improving predictive accuracy. However, the proposed methods a...
In recent decades, the traditional monopolistic energy exchange market has been replaced by deregulated, competitive marketplaces in which electricity may be purchased and sold at market prices like any other commodity. As a result, the deregulation...
The widespread adoption of electricity market trading platforms has enhanced the standardization and transparency of trading processes. As markets become more liberalized, regulatory policies are phasing out protective electricity pricing mechanisms,...
The uncertainty that dominates in the functioning of the electricity market is of great significance and arises, generally, because of the time imbalance in electricity consumption rates and power plants’ production capacity, as well as the inf...
The increasing adoption of highly variable renewable energy has introduced unprecedented volatility into the National Electricity Market (NEM), rendering traditional linear price forecasting models insufficient. The Australian Energy Market Operator...
In recent years, time series forecasting has become an essential tool for stock market analysts to make informed decisions regarding stock prices. The present research makes use of various exponential smoothing forecasting methods. These include expo...
Since the electricity market liberalisation of the mid-1990s, forecasting energy demand and prices in competitive markets has become of primary importance for energy suppliers, market regulators and policy makers. In this paper, we propose a non-para...
This study investigates a large language model driven (LLM) framework for intelligent preprocessing and short-term electricity price forecasting in the Australian National Electricity Market (NEM). By integrating unstructured news features, weather s...
The present paper considers the problem of choosing among a collection of competing electricity price forecasting models to address a stochastic decision-making problem. We propose an event-based evaluation framework applicable to any optimization pr...
In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized fr...
The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on el...
Our research involves analyzing the latest models used for electricity price forecasting, which include both traditional inferential statistical methods and newer deep learning techniques. Through our analysis of historical data and the use of multip...
The imbalance cost pass-through (ICPT) is a flexible component of the incentive-based regulation (IBR) that empowers power producers to adjust tariffs in response to variable fuel prices, thereby enhancing the economic resilience of electricity gener...
A smart grid is an intelligent electricity network that allows efficient electricity distribution from the source to consumers through telecommunication technology. The legacy smart grid follows the centralized oligopoly marketplace for electricity t...
This paper describes the development of a deep neural network architecture based on transformer encoder blocks and Time2Vec layers for the prediction of electricity prices several steps ahead (8 h), from a probabilistic approach, to feed future decis...
In response to the inherent challenges of generating cost-effective electricity consumption schedules for dynamic systems, this paper espouses the use of GBM or Gradient Boosting Machine-based models for electricity price forecasting. These models ar...
This paper aims to improve the forecasting of electricity market prices by incorporating the characteristics of electricity market prices that are discretely affected by the fuel cost per unit, the unit generation cost of the large-scale generators,...
Electricity price is a key influencer in the electricity market. Electricity market trades by each participant are based on electricity price. The electricity price adjusted with the change in supply and demand relationship can reflect the real value...
This study proposes and evaluates a data-driven framework for short-term System Marginal Price (SMP) forecasting in the Chilean National Electric System (NES), a power system characterized by high penetration of variable renewable generation and pers...
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