Electricity demand forecasting by multi-task learning

Fiot, Jean-Baptiste; Dinuzzo, Francesco

doi:10.1109/ptc.2017.7980955

Cited by 17 publications

(16 citation statements)

References 8 publications

(13 reference statements)

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“…It is well known that load demand curves at the transmission level exhibit high correlation among nearby locations, and thus share similar temporal patterns. This property has been widely used by load forecasting and data cleansing works; see e.g., [13,[17][18][19]. Several factors play a role in leading to this similarity, including weather conditions (i.e.…”

Section: Spatio-temporal Load Recoverymentioning

confidence: 99%

Enhancing the Spatio-Temporal Observability of Residential Loads

Lin¹,

Zhu²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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Enhancing the spatio-temporal observability of residential loads is crucial for achieving secure and efficient operations in distribution systems with increasing penetration of distributed energy resources (DERs). This paper presents a joint inference framework for residential loads by leveraging the real-time measurements from distribution-level sensors. Specifically, smart meter data is available for almost every load with unfortunately low temporal resolution, while distribution synchrophasor data is at very fast rates yet available at limited locations. By combining these two types of data with respective strengths, the problem is cast as a matrix recovery one with much less number of observations than unknowns. To improve the recovery performance, we introduce two regularization terms to promote a lowrank plus sparse structure of the load matrix via a difference transformation. Accordingly, the recovery problem can be formulated as a convex optimization one which is efficiently solvable. Numerical tests using real residential load data demonstrate the effectiveness of our proposed approaches in identifying appliance activities and recovering the PV output profiles. arXiv:1910.00984v2 [eess.SY]

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Section: Spatio-temporal Load Recoverymentioning

confidence: 99%

Enhancing the Spatio-Temporal Observability of Residential Loads

Lin¹,

Zhu²

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Similarly, authors in [29] study the interruption prediction with weather conditions by simulating a NN. Besides these work, the authors in [30] explore kernel-based learning techniques to forecast both commercial and industrial load. In addition, feature selection techniques are applied in [31] to predict electricity load and prices.…”

Section: B Literature Reviewmentioning

confidence: 99%

A Sparse Linear Model and Significance Test for Individual Consumption Prediction

Zhang

Weng

et al. 2017

IEEE Trans. Power Syst.

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Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well.Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting for heterogeneity between individual users. In this paper, we propose a method to improve prediction accuracy of individual users by adaptively exploring sparsity in historical data and leveraging predictive relationship between different users. Sparsity is captured by popular least absolute shrinkage and selection estimator, while user selection is formulated as an optimal hypothesis testing problem and solved via a covariance test. Using real world data from PG&E, we provide extensive simulation validation of the proposed method against well-known techniques such as support vector machine, principle component analysis combined with linear regression, and random forest. The results demonstrate that our proposed methods are operationally efficient because of linear nature, and achieve optimal prediction performance. Index TermsLoad forecasting, least absolute shrinkage and selection, sparse autoregressive model, significance test NOMENCLATURE y t Consumption at time t.

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“…Recently, a model based on enhanced deep neural network (EDNN) is developed to predict week and year ahead electrical energy consumption [7]. In [8], a framework based on a multi-task regressor is proposed to predict electrical energy consumption based on the recorded energy consumption data by the smart meters. Sideratos et al [9] proposed a load prediction model based on a hybrid DNN (HDNN).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

et al. 2020

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Electric energy consumption forecasting enables distribution system operators to perform efficient energy management by flexibly engaging energy consumers under the intelligent demand-response program in the smart grid (SG). With this motivation, in this paper, a fast and accurate hybrid electrical energy forecasting (FA-HELF) framework is developed. The proposed framework integrates two modules with support vector machine (SVM) based forecaster. These modules are data pre-processing and feature engineering, and modified enhanced differential evolution (mEDE) based optimizer. First, feature selection algorithms like random forests and relief-F are combined to devise a hybrid feature selection algorithm to alleviate redundancy. Secondly, for feature extraction, a radial basis Kernel-based principal component analysis algorithm is employed to eliminate the dimensionality reduction problem. Finally, to conduct accurate and fast electrical energy consumption forecasting, the mEDE based optimizer is integrated with the SVM based forecaster. The resulting FA-HELF framework is tested on publicly available independent system operator New England (ISO-NE) control area hourly load data. The results demonstrate that the FA-HELF framework is robust and shows significant improvements when compared to other benchmark frameworks in terms of accuracy and convergence speed. INDEX TERMS Electrical energy consumption forecasting, energy management, smart grid, grey correlation analysis, differential evolution, radial basis kernel-based principal component analysis, support vector machine.

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Electricity demand forecasting by multi-task learning

Cited by 17 publications

References 8 publications

Enhancing the Spatio-Temporal Observability of Residential Loads

Enhancing the Spatio-Temporal Observability of Residential Loads

A Sparse Linear Model and Significance Test for Individual Consumption Prediction

A Hybrid Approach for Energy Consumption Forecasting With a New Feature Engineering and Optimization Framework in Smart Grid

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