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Giannakis, “Electricity market forecasting via lowrank multikernel learning
 IEEE J. Sel. Topics Sig. Proc
, 2014
"... Abstract—The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Dayahead ..."
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Abstract—The smart grid vision entails advanced information technology and data analytics to enhance the efficiency, sustainability, and economics of the power grid infrastructure. Aligned to this end, modern statistical learning tools are leveraged here for electricity market inference. Dayahead price forecasting is cast as a lowrank kernel learning problem. Uniquely exploiting the market clearing process, congestion patterns are modeled as rankone components in the matrix of spatiotemporally varying prices. Through a novel nuclear normbased regularization, kernels across pricing nodes and hours can be systematically selected. Even though marketwide forecasting is beneficial from a learning perspective, it involves processing highdimensional market data. The latter becomes possible after devising a blockcoordinate descent algorithm for solving the nonconvex optimization problem involved. The algorithm utilizes results from blocksparse vector recovery and is guaranteed to converge to a stationary point. Numerical tests on real data from the Midwest ISO (MISO) market corroborate the prediction accuracy, computational efficiency, and the interpretative merits of the developed approach over existing alternatives. Index Terms—Blockcoordinate descent, dayahead energy prices, graph Laplacian, kernelbased learning, learning, lowrank matrix, multikernel learning, nuclear norm regularization. I.
Rank regularization and bayesian inference for tensor completion and extrapolation. arXiv preprint arXiv:1301.7619
, 2013
"... factors capturing the tensor’s rank is proposed in this paper, as the key enabler for completion of threeway data arrays with missing entries. Set in a Bayesian framework, the tensor completion method incorporates prior information to enhance its smoothing and prediction capabilities. This probabil ..."
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factors capturing the tensor’s rank is proposed in this paper, as the key enabler for completion of threeway data arrays with missing entries. Set in a Bayesian framework, the tensor completion method incorporates prior information to enhance its smoothing and prediction capabilities. This probabilistic approach can naturally accommodate general models for the data distribution, lending itself to various fitting criteria that yield optimum estimates in the maximumaposteriori sense. In particular, two algorithms are devised for Gaussian and Poissondistributed data, that minimize the rankregularized leastsquares error and KullbackLeibler divergence, respectively. The proposed technique is able to recover the “groundtruth ” tensor rank when tested on synthetic data, and to complete brain imaging and yeast gene expression datasets with 50 % and 15 % of missing entries respectively, resulting in recovery errors at and. Index Terms—Bayesian inference, lowrank, missing data, Poisson process, tensor. I.
Giannakis, “Load curve data cleansing and imputation via sparsity and low rank
 IEEE Trans. Smart Grid
, 2013
"... Abstract—The smart grid vision is to build an intelligent power network with an unprecedented level of situational awareness and controllability over its services and infrastructure. This paper advocates statistical inference methods to robustify power monitoring tasks against the outlier effects o ..."
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Abstract—The smart grid vision is to build an intelligent power network with an unprecedented level of situational awareness and controllability over its services and infrastructure. This paper advocates statistical inference methods to robustify power monitoring tasks against the outlier effects owing to faulty readings and malicious attacks, as well as against missing data due to privacy concerns and communication errors. In this context, a novel load cleansing and imputation scheme is developed leveraging the low intrinsicdimensionality of spatiotemporal load profiles and the sparse nature of “bad data. ” A robust estimator based on principal components pursuit (PCP) is adopted, which effects a twofold sparsitypromoting regularization through annorm of the outliers, and the nuclear norm of the nominal load profiles. Upon recasting the nonseparable nuclear norm into a form amenable to decentralized optimization, a distributed (D) PCP algorithm is developed to carry out the imputation and cleansing tasks using networked devices comprising the sotermed advanced metering infrastructure. If DPCP converges and a qualification inequality is satisfied, the novel distributed estimator provably attains the performance of its centralized PCP counterpart, which has access to all networkwide data. Computer simulations and tests with real load curve data corroborate the convergence and effectiveness of the novel DPCP algorithm. Index Terms—Advancedmetering infrastructure, distributed algorithms, load curve cleansing and imputation, principal components pursuit, smart grid. I.
Decentralized Learning for Wireless Communications and Networking
"... Abstract This chapter deals with decentralized learning algorithms for innetwork processing of graphvalued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternatingdirection method of multipliers (ADMM) so as to gain the ..."
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Abstract This chapter deals with decentralized learning algorithms for innetwork processing of graphvalued data. A generic learning problem is formulated and recast into a separable form, which is iteratively minimized using the alternatingdirection method of multipliers (ADMM) so as to gain the desired degree of parallelization. Without exchanging elements from the distributed training sets and keeping internode communications at affordable levels, the local (pernode) learners consent to the desired quantity inferred globally, meaning the one obtained if the entire training data set were centrally available. Impact of the decentralized learning framework to contemporary wireless communications and networking tasks is illustrated through case studies including target tracking using wireless sensor networks, unveiling Internet traffic anomalies, power system state estimation, as well as spectrum cartography for wireless cognitive radio networks.
Automated Recovery of Compressedly Observed Sparse Signals From Smooth Background
"... Abstract—We propose a Bayesian based algorithm to recover sparse signals from compressed noisy measurements in the presence of a smooth background component. This problem is closely related to robust principal component analysis and compressive sensing, and is found in a number of practical areas. ..."
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Abstract—We propose a Bayesian based algorithm to recover sparse signals from compressed noisy measurements in the presence of a smooth background component. This problem is closely related to robust principal component analysis and compressive sensing, and is found in a number of practical areas. The proposed algorithm adopts a hierarchical Bayesian framework for modeling, and employs approximate inference to estimate the unknowns. Numerical examples demonstrate the effectiveness of the proposed algorithm and its advantage over the current stateoftheart solutions. Index Terms—Bayesian algorithm, compressive sensing, robust principal component analysis.
LowRank Kernel Learning for Electricity Market Inference
"... Abstract—Recognizing the importance of smart grid data analytics, modern statistical learning tools are applied here to wholesale electricity market inference. Market clearing congestion patterns are uniquely modeled as rankone components in the matrix of spatiotemporally correlated prices. Upon po ..."
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Abstract—Recognizing the importance of smart grid data analytics, modern statistical learning tools are applied here to wholesale electricity market inference. Market clearing congestion patterns are uniquely modeled as rankone components in the matrix of spatiotemporally correlated prices. Upon postulating a lowrank matrix factorization, kernels across pricing nodes and hours are systematically selected via a novel methodology. To process the highdimensional market data involved, a blockcoordinate descent algorithm is developed by generalizing blocksparse vector recovery results to the matrix case. Preliminary numerical tests on real data corroborate the prediction merits of the developed approach. I.