Support vector machine for day ahead electricity price forecasting

Razak, Intan Azmira binti Wan Abdul; Abidin, Izham Zainal; Yap, Keem Siah; Rahman, Titik Khawa Abdul; Lada, Musa Yusup; Ramani, Anis Niza; Nasir, Mohamad Na'im Mohd; Ahmad, Arfah

doi:10.1063/1.4915865

Cited by 4 publications

(4 citation statements)

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“…Some shortcomings exist in AI models as well. For example, from [8], it can be seen that when neural network and fuzzy logic models were used in electricity markets' forecasts they experienced generalization problems and proved to be unsuitable for unseen data. But fortunately, other AI-based approaches such as support vector machines (SVMs), can help in such cases by reducing the model complexities.…”

Section: Key Applications Of Ai-based Forecast Methodsmentioning

confidence: 99%

“…But fortunately, other AI-based approaches such as support vector machines (SVMs), can help in such cases by reducing the model complexities. In [8], [31] a smaller training data for price forecasts using SVM helped to resolve the drawbacks of neural networks. In this section, applications of AI-based methods in EBMs are investigated for different roles.…”

Section: Key Applications Of Ai-based Forecast Methodsmentioning

confidence: 99%

“…But so far, studies have mainly been conducted for AIbased estimations in day-ahead markets. From the review literature such as [6]- [8], it can be seen that such studies for the case of balancing markets are rather scarce. Even though in [9], a range of models was benchmarked to forecast balancing market prices, but AI-based methods were not targeted.…”

Section: Introductionmentioning

confidence: 99%

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Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

Hameed

Hashemi

Traholt

2021

2021 22nd IEEE International Conference on Industrial Technology (ICIT)

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Rising environmental concerns are integrating more renewables in power systems. This increase introduces uncertainty in generation and makes it challenging to maintain a balance between demand and supply. To avoid balancing problems and consequent stability issues, better forecast models are needed as traditional techniques are not fully equipped to deal with these new challenges. Thus, artificial intelligence (AI) based forecast techniques are gaining potential recognition in the realm of electricity markets. This paper aims at investigating the state-of-art of AI applications for price forecasts in electricity balancing markets (EBMs). The focus of previous studies extended in this direction has been towards the dayahead markets, whereas studies targeting EBMs are rather scarce. This paper shows how AI-based forecasts support EBMs modeling, resulting in more secure grid integration of distributed technologies. The benefits driven from such forecasts by market participants like brokers and customers are also investigated.

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Section: Key Applications Of Ai-based Forecast Methodsmentioning

confidence: 99%

Section: Key Applications Of Ai-based Forecast Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

Hameed

Hashemi

Traholt

2021

2021 22nd IEEE International Conference on Industrial Technology (ICIT)

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“…However, since ASMs are relatively new as compared to dayahead spot markets, their forecast literature is scarce. AI-based models have been extensively used to forecast prices of various other markets in the electricity sector such as day-ahead market and balancing markets [63][64][65], however, ASMs have not been targeted. Moreover, since ASs products in different regions of the world are diverse and ASMs have unique market requirements in contrast to the energy markets, econometrics research in this direction is also scarce.…”

Section: Bess Service Stackingmentioning

confidence: 99%

Investigating the participation of battery energy storage systems in the Nordic ancillary services markets from a business perspective

Hameed

Træholt

Hashemi

2023

Journal of Energy Storage

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Research on Cost Prediction of Power Transmission and Transformation Project Based on Combination Prediction Model

Wang

Liao

Guo

et al. 2021

IOP Conf. Ser.: Earth Environ. Sci.

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Based on the project division of power transmission and transformation projects, this paper analyzes a large number of practical engineering cases, and summarizes the influencing factors of substation and overhead line project cost combined with the practical engineering experience of many experts, A number of cost control indicators are extracted, and a combination prediction model of project cost based on neural network and support vector machine is constructed. The actual project data are taken as samples for empirical research. The results show that the combination prediction model is feasible and accurate for the prediction of power transmission and transformation project cost, which provides a new idea and implementation method for the prediction of power transmission and transformation project cost.

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Support vector machine for day ahead electricity price forecasting

Cited by 4 publications

References 0 publications

Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

Applications of AI-Based Forecasts in Renewable Based Electricity Balancing Markets

Investigating the participation of battery energy storage systems in the Nordic ancillary services markets from a business perspective

Research on Cost Prediction of Power Transmission and Transformation Project Based on Combination Prediction Model

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