Forecasting balancing market prices using Hidden Markov Models

Dimoulkas, Ilias; Amelin, Mikael; Hesamzadeh, Mohammad Reza

doi:10.1109/eem.2016.7521229

Cited by 12 publications

(14 citation statements)

References 11 publications

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“…While market states are very difficult to predict punctually, the forecasting is still useful if it provides good probabilistic information. In any case, trivial forecasts can be hard to be surpassed in performance [27]. In this paper, we used a simple probabilistic model based on historical data for simulating plausible outcomes of the market (see Section 6.1.3).…”

Section: Market Simulation Techniquesmentioning

confidence: 99%

VIRTUS Project: A Scalable Aggregation Platform for the Intelligent Virtual Management of Distributed Energy Resources

Bianchi¹,

Filippo

Magnani³

et al. 2021

Energies

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The VIRTUS project aims to create a Virtual Power Plant (VPP) prototype coordinating the Distributed Energy Resources (DERs) of the power system and providing services to the system operators and the various players of the electricity markets, with a particular focus on the industrial sector agents. The VPP will be able to manage a significant number of DERs and simulate realistic plants, components, and market data to study different operating conditions and the future impact of the policy changes of the Balancing Markets (BM). This paper describes the project’s aim, the general structure of the proposed framework, and its optimization and simulation modules. Then, we assess the scalability of the optimization module, designed to provide the maximum possible flexibility to the system operators, exploiting the simulation module of the VPP.

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Section: Market Simulation Techniquesmentioning

confidence: 99%

VIRTUS Project: A Scalable Aggregation Platform for the Intelligent Virtual Management of Distributed Energy Resources

Bianchi¹,

Filippo

Magnani³

et al. 2021

Energies

View full text Add to dashboard Cite

show abstract

“…HMM is a double stochastic process that introduces the hidden states with the unobservable underlying stochastic process. HMM has been applied to time-series forecasting in different domains such as forecasting prices of stock and option [36][37][38], commodity market [39], electricity [40,41], crude oil [42], and currency exchange rate [43] from 2007 to 2018. As the real-life market is complex and highly volatile with nonlinear relationships, HMM helps model the time-series data with the hidden states.…”

Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

“…Hence, the model is suitable for dynamic pricing when the time-series data are non-stationary and high in volatility. It is noticed that HMM has a better performance compared to regression models such as GARCH when the data are high in volatility and has a larger sample size [38,103]. HMM can also outperform some Deep Learning (DL) models due to its utterly probabilistic state architecture [36,104].…”

Section: Hidden Markov Model (Hmm)mentioning

confidence: 99%

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

et al. 2021

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Audience attention is vital in Digital Signage Advertising (DSA), as it has a significant impact on the pricing decision to advertise on those media. Various environmental factors affect the audience attention level toward advertising signage. Fixed-price strategies, which have been applied in DSA for pricing decisions, are generally inefficient at maximizing the potential profit of the service provider, as the environmental factors that could affect the audience attention are changing fast and are generally not considered in the current pricing solutions in a timely manner. Therefore, the time-series forecasting method is a suitable pricing solution for DSA, as it improves the pricing decision by modeling the changes in the environmental factors and audience attention level toward signage for optimal pricing. However, it is difficult to determine an optimal price forecasting model for DSA with the increasing number of available time-series forecasting models in recent years. Based on the 84 research articles reviewed, the data characteristics analysis in terms of linearity, stationarity, volatility, and dataset size is helpful in determining the optimal model for time-series price forecasting. This paper has reviewed the widely used time-series forecasting models and identified the related data characteristics of each model. A framework is proposed to demonstrate the model selection process for dynamic pricing in DSA based on its data characteristics analysis, paving the way for future research of pricing solutions for DSA.

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“…They conclude that the developed model combination is suitable to use for the generation of real-time balancing power price scenarios. Klaeboe, Eriksrud, and Fleten (2013) benchmark time-series-based forecasting models, and Dimoulkas, Amelin, and Hesamzadeh (2016) apply a hidden Markov model to forecast balancing reserve market prices for the Nordic market. They argue that activation of the balancing reserve occurs randomly and an activationbased price is therefore hardly predictable.…”

Section: Related Literaturementioning

confidence: 99%

Modeling of frequency containment reserve prices with econometrics and artificial intelligence

Kraft

Keles

Fïchtner

2020

Journal of Forecasting

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The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from econometrics and artificial intelligence and set up a forecasting framework based on autoregressive and exogenous factors. We use SARIMAX models as well as neural networks with different structures and forecast based on a rolling one-step forecast with reestimation of the models. It turns out that the naive forecast performs reasonably well but is outperformed by the more advanced models. In addition, neural network approaches outperform the econometric approach in terms of forecast quality, whereas for the further use of the generated models the econometric approach has advantages in terms of explaining price drivers. For the present application, more advanced configurations of the neural networks are not able to further improve the forecasting performance.

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Forecasting balancing market prices using Hidden Markov Models

Cited by 12 publications

References 11 publications

VIRTUS Project: A Scalable Aggregation Platform for the Intelligent Virtual Management of Distributed Energy Resources

VIRTUS Project: A Scalable Aggregation Platform for the Intelligent Virtual Management of Distributed Energy Resources

Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising

Modeling of frequency containment reserve prices with econometrics and artificial intelligence

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