Online distributed learning in wind power forecasting

Sommer, Benedikt; Messner, Jakob W.; Obst, David

doi:10.1016/j.ijforecast.2020.04.004

Cited by 21 publications

(11 citation statements)

References 29 publications

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“…Z Ai M −1 is obtained by adapting the protocol in ( 10)- (11). In this case, the value of r is more restrictive because we need to ensure that agent i does not obtain both Y Ai M −1 and MY Ai .…”

Section: B Formulation Of the Collaborative Forecasting Modelmentioning

confidence: 99%

“…Moreover, the central node can also recover the original and private data. Sommer et al [11] considered an encryption layer, which consists of multiplying the data by a random matrix. However, the focus of this work was not data privacy, but rather online learning, and the private data are revealed to the central agent who performs intermediary computations.…”

Section: Introductionmentioning

confidence: 99%

“…With our focus on privacy-preserving protocols for very short-term forecasting with the VAR model, the main research outcome from this work is a novel combination of data transformation and decomposition-based methods so that the VAR model is fitted in another feature space without decreasing the forecast skill (which contrasts with [12]). The main advantage of this combination is that the ADMM algorithm is not affected and therefore: (a) asynchronous communication between peers can be addressed while fitting the model; (b) a flexible privacy-preserving collaborative model can be implemented using two different schemes, centralized communication with a neutral node and peer-to-peer communication, and in a way that original data cannot be recovered by central node or peers (this represents a more robust approach compared to the ADMM implementation in [9], [11]).…”

Section: Introductionmentioning

confidence: 99%

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Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Gonçalves

Bessa

2021

IEEE Trans. Sustain. Energy

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Data exchange between multiple renewable energy power plant owners can lead to an improvement in forecast skill thanks to the spatio-temporal dependencies in time series data. However, owing to business competitive factors, these different owners might be unwilling to share their data. In order to tackle this privacy issue, this paper formulates a novel privacypreserving framework that combines data transformation techniques with the alternating direction method of multipliers. This approach allows not only to estimate the model in a distributed fashion but also to protect data privacy, coefficients and covariance matrix. Besides, asynchronous communication between peers is addressed in the model fitting, and two different collaborative schemes are considered: centralized and peer-topeer. The results for a solar energy dataset show that the proposed method is robust to privacy breaches and communication failures, and delivers a forecast skill comparable to a model without privacy protection.

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Section: B Formulation Of the Collaborative Forecasting Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Gonçalves

Bessa

2021

IEEE Trans. Sustain. Energy

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“…As a result, distributed learning algorithms have gained prominence in wind power forecasting. Online ADMM and mirror-descent inspired algorithms were implemented by Sommer et al [53] to forecast wind power generation in a distributed manner. Each wind operator site that wanted to perform the forecast was a central agent.…”

Section: Renewable Energy Forecastmentioning

confidence: 99%

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Gholizadeh

Musilek

2021

Energies

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In recent years, machine learning methods have found numerous applications in power systems for load forecasting, voltage control, power quality monitoring, anomaly detection, etc. Distributed learning is a subfield of machine learning and a descendant of the multi-agent systems field. Distributed learning is a collaboratively decentralized machine learning algorithm designed to handle large data sizes, solve complex learning problems, and increase privacy. Moreover, it can reduce the risk of a single point of failure compared to fully centralized approaches and lower the bandwidth and central storage requirements. This paper introduces three existing distributed learning frameworks and reviews the applications that have been proposed for them in power systems so far. It summarizes the methods, benefits, and challenges of distributed learning frameworks in power systems and identifies the gaps in the literature for future studies.

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“…Note that, by distributed, we here mean both in the geographical and ownership sense, i.e., the data potentially valuable to a given agent of the energy system is actually collected and owned by other agents. Therefore, some have pushed forward proposals towards distributed and privacy-preserving learning (Zhang and Wang , 2018;Sommer et al ., 2021), as a way to get the benefits from such distributed data, without revealing the private information of the agents involved. Beyond energy applications, this approach is generally known as federated learning (Li et al ., 2020), with substantial developments over the last few years.…”

Section: Introductionmentioning

confidence: 99%

Regression markets and application to energy forecasting

Han¹,

Kazempour²

2021

Preprint

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Energy forecasting has attracted enormous attention over the last few decades, with novel proposals related to the use of heterogeneous data sources, probabilistic forecasting, online learning, etc. A key aspect that emerged is that learning and forecasting may highly benefit from distributed data, though not only in the geographical sense. That is, various agents collect and own data that may be useful to others. In contrast to recent proposals that look into distributed and privacy-preserving learning (incentive-free), we explore here a framework called regression markets. There, agents aiming to improve their forecasts post a regression task, for which other agents may contribute by sharing their data for their features and get monetarily rewarded for it. The market design is for regression models that are linear in their parameters, and possibly separable, with estimation performed based on either batch or online learning. Both in-sample and out-of-sample aspects are considered, with markets for fitting models in-sample, and then for improving genuine forecasts out-of-sample. Such regression markets rely on recent concepts within interpretability of machine learning approaches and cooperative game theory, with Shapley additive explanations. Besides introducing the market design and proving its desirable properties, application results are shown based on simulation studies (to highlight the salient features of the proposal) and with real-world case studies.

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Online distributed learning in wind power forecasting

Cited by 21 publications

References 29 publications

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Privacy-Preserving Distributed Learning for Renewable Energy Forecasting

Distributed Learning Applications in Power Systems: A Review of Methods, Gaps, and Challenges

Regression markets and application to energy forecasting

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