Smart grid load forecasting using online support vector regression

Vrablecová, Petra; Ezzeddine, Anna Bou; Rozinajová, Viera; Šárik, Slavomír; Sangaiah, Arun Kumar

doi:10.1016/j.compeleceng.2017.07.006

Cited by 133 publications

(61 citation statements)

References 15 publications

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“…The second benchmarking base model is support vector regression. Since the method of support vector machines(SVR) was proposed in 1995 [33], SVR is a popular machine learning model proposed for forecasting tasks even recently [34][35][36]. It has also been used as a benchmark for comparison in various studies in the survey of related works [14,18].…”

Section: Benchmarking Base Modelmentioning

confidence: 99%

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

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With the rising focus on building energy big data analysis, there lacks a framework for raw data preprocessing to answer the question of how to handle the missing data in the raw data set. This study presents a methodology and framework for building energy consumption raw data forecasting. A case building is used to forecast the energy consumption by using deep recurrent neural networks. Four different methodologies to impute missing data in the raw data set are compared and implemented. The question of sensitivity of gap size and available data percentage on the imputation accuracy was tested. The cleaned data were then used for building energy forecasting. While the existing studies explored only the use of small recurrent networks of 2 layers and less, the question of whether a deep network of more than 2 layers would be performing better for building energy consumption forecasting should be explored. In addition, the problem of overfitting has been cited as a significant problem in using deep networks. In this study, the deep recurrent neural network is then used to explore the use of deeper networks and their regularization in the context of an energy load forecasting task. The results show a mean absolute error of 2.1 can be achieved through the 2*32 gated neural network model. In applying regularization methods to overcome model overfitting, the study found that weights regularization did indeed delay the onset of overfitting.

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Section: Benchmarking Base Modelmentioning

confidence: 99%

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

et al. 2019

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“…], [14], [25], [42], [45], [78], [101], [110], [122], [124], [132], [156], [164], [171], [178], [187], [210], [211], [213], [228], [237], [242], [256]), Support Vector Machines (SVM) (21) ( [? ], [36], [53], [57], [65], [78], [79], [106], [115], [117], [122], [157], [159], [166], [187], [193], [203], [227], [240], [253], [256]), autoregressive integrated moving average (ARIMA) (13) ( [6], [19], [32], [42], [53], [78], [90],…”

Section: Sms Resultsmentioning

confidence: 99%

Smart Grids Data Analysis: A Systematic Mapping Study

Rossi

Chren

2020

IEEE Trans. Ind. Inf.

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Article under review. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.The article is structured as follows. In section II, we provide an overview of Smart Grids concepts: mainly architecture and components of the SG infrastructure. In section III, we define the goals, needs, process and research questions for the Systematic Mapping Study on data analysis in the SG context. In section IV, we present the results from the overall SMS process, divided into answers to the main research questions set. In Section V, we discuss the main threats to validity of the current study. Section VI provides the final conclusions.

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“…Framing can be done either with constant or with variable frame length [35,47]. In the state-based baseline NILM approach, in order to estimate the device consumption on a state level, a regression algorithm instead of a classification algorithm is used [48,49], while classification is used in event-based approaches to detect devices' on/off states [39,45,46].…”

Section: Baseline Nilm Architecturementioning

confidence: 99%

Robust energy disaggregation using appliance-specific temporal contextual information

Schirmer

Mporas

Sheikh-Akbari

2020

EURASIP J. Adv. Signal Process.

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An extension of the baseline non-intrusive load monitoring approach for energy disaggregation using temporal contextual information is presented in this paper. In detail, the proposed approach uses a two-stage disaggregation methodology with appliance-specific temporal contextual information in order to capture time-varying power consumption patterns in low-frequency datasets. The proposed methodology was evaluated using datasets of different sampling frequency, number and type of appliances. When employing appliance-specific temporal contextual information, an improvement of 1.5% up to 7.3% was observed. With the two-stage disaggregation architecture and using appliance-specific temporal contextual information, the overall energy disaggregation accuracy was further improved across all evaluated datasets with the maximum observed improvement, in terms of absolute increase of accuracy, being equal to 6.8%, thus resulting in a maximum total energy disaggregation accuracy improvement equal to 10.0%.

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Smart grid load forecasting using online support vector regression

Cited by 133 publications

References 15 publications

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Building Energy Consumption Raw Data Forecasting Using Data Cleaning and Deep Recurrent Neural Networks

Smart Grids Data Analysis: A Systematic Mapping Study

Robust energy disaggregation using appliance-specific temporal contextual information

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