Hourly electricity price forecasting with NARMAX

McHugh, Catherine; Coleman, Sonya; Kerr, Dermot

doi:10.1016/j.mlwa.2022.100383

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…Concerning the time series approach, in the last years some studies have addressed the electricity price forecasting using a hybrid approach of statistical and regression models, based on a NARMAX model [9], or ARMAX Models based on a linear regression where functional parameters operate on functional variables [10]. Other references employ dynamic trees and random forest statistical techniques [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Spot Price Modeling and Forecasting in European Markets

2022

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In many competitive electricity markets around the world, the dynamic behavior of hourly electricity prices is subject to significant uncertainty and volatility due to electricity demand, availability of generation sources, fuel costs, and power plant availability. This work is devoted to describing and comparing the dynamics of electricity prices for some markets in Europe, selecting the five countries representing the largest economies in Western Europe (France, Germany, Italy, Spain, and the United Kingdom). Additionally, Denmark is included in the study to assess whether the size of the country is a determinant of price behavior. The six datasets of hourly price series, which exhibits a strong daily seasonality, are modelled using the most relevant well-known statistical models for time series analysis: ARIMA models and different versions of GARCH models. The comparison of the estimated models’ parameters, the analysis of outliers’ rate of appearance and the evaluation of out-of-sample one-day-ahead forecast let us draw some insightful similarities and dissimilarities between the analyzed countries.

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Section: Literature Reviewmentioning

confidence: 99%

Electricity Spot Price Modeling and Forecasting in European Markets

2022

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“…The said research category needs to be revised to measure the appetite for clean energy intake in nations in the context of de-carbonization. On the other hand, as stated, electricity price forecasting has seen considerable traction in literature [ [10] , [11] , [12] , [13] ], predominantly owing to thwarting the supply-demand vagary in extreme weather conditions, geopolitical conflicts, etc. The influence of renewables on electricity price prediction has also been explored [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI

Ghosh,

Alfaro-Cortés,

Gámez

et al. 2024

Heliyon

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“…In the solar energy realm, these methodologies are widespread, incorporating diverse techniques like Markov Chains, [ 24 ] fuzzy logic, [ 25 ] and auto‐regressive [ 26 ] models such as Nonlinear Autoregressive model with eXogenous inputs (NARX) [ 27 ] and Nonlinear Autoregressive Moving average with eXogenous inputs (NARMAX). [ 28 ] The study on daylight utilization for energy saving in Karachi's buildings, which identified optimal dome areas for energy efficiency, serves as a practical example of how forecasting can aid in enhancing building energy conservation. [ 29 ] Despite generally being less complex than physical models, the reliance on statistical methods on historical data facilitates more detailed modeling of specific plant characteristics.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

Hanif,

Siddique,

et al. 2024

Advcd Theory and Sims

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Effective solar energy utilization demands improvements in forecasting due to the unpredictable nature of solar irradiance (SI). This study introduces and rigorously tests two innovative forecasting models across different locations: the Sequential Deep Artificial Neural Network (SDANN) and the Deep Hybrid Random Forest Gradient Boosting (RFGB). SDANN, leveraging deep learning, aims to identify complex patterns in weather data, while RFGB, combining Random Forest and Gradient Boosting, proves more effective by offering a superior balance of efficiency and accuracy. The research highlights the SDANN model's deep learning capabilities along with the RFGB model's unique blend and their comparative success over existing models such as eXtreme Gradient Boosting (XGBOOST), Categorical Boosting (CatBOOST), Gated Recurrent Unit (GRU), and a K‐Nearest Neighbors (KNN) and XGBOOST hybrid. With the lowest Mean Squared Error (147.22), Mean Absolute Error (8.77), and a high R2 value (0.80) in a studied region, RFGB stands out. Additionally, detailed ablation studies on meteorological feature impacts on model performance further enhance accuracy and adaptability. By integrating cutting‐edge AI in SI forecasting, this research not only advances the field but also sets the stage for future renewable energy strategies and global policy‐making.

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Hourly electricity price forecasting with NARMAX

Cited by 13 publications

References 15 publications

Electricity Spot Price Modeling and Forecasting in European Markets

Electricity Spot Price Modeling and Forecasting in European Markets

Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI

Enhancing Solar Forecasting Accuracy with Sequential Deep Artificial Neural Network and Hybrid Random Forest and Gradient Boosting Models across Varied Terrains

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