An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation

Saeedi, Ramyar; Sadanandan, Sajan K.; Srivastava, Anurag K.; Davies, Kevin L.; Gebremedhin, Assefaw H.

doi:10.1109/tii.2021.3060898

Cited by 37 publications

(15 citation statements)

References 26 publications

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“…However, parametric models are generally limited to addressing simple problems, and the accuracy of their fundamental mapping functions can be questionable. Both parametric models (e.g., linear regression and multilayer perceptron) and non-parametric models (e.g., decision tree and random forest) have been adopted for appliance classification [3], [130], [183], [188], [208], [211], [212]. Although a study in [3] demonstrated that the k-Nearest Neighbors (KNN) model is most efficient for energy disaggregation compared to decision trees, discriminant analysis, and Support Vector Machine (SVM), it is important to note that these results were based on a specific dataset.…”

Section: Yinyan Liumentioning

confidence: 99%

“…In this context, the correct disaggregated load 𝒚 for the aggregate load 𝒙 is known in advance. Various supervised learning approaches, such as linear regression [212], genetic algorithms [153], [200], [249], SVM [122], [188], [189], [250], random decision forest [122], [183], [212], [251], hidden Markov models (HMM) [215], [252], decision trees [3], [212], [253], naive Bayes classifier [254], kNN [3], [171], [188], and deep learning neural networks [109], [110], [112], [116], [197], [240], [255], have been applied in the NILM literature. As outlined in Table IV, supervised NILM is mainly used for regression and classification tasks.…”

Section: ) Supervised Approachesmentioning

confidence: 99%

See 1 more Smart Citation

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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Non-Intrusive Load Monitoring (NILM) is pivotal in today's energy landscape, offering vital solutions for energy conservation and efficient management. Its growing importance in enhancing energy savings and understanding consumer behavior makes it a pivotal technology for addressing global energy challenges. This paper delivers an in-depth review of NILM, highlighting its critical role in smart homes and smart grids. The significant contributions of this study are threefold: Firstly, it compiles a comprehensive global dataset table, providing a valuable tool for researchers and engineers to select appropriate datasets for their NILM studies. Secondly, it categorizes NILM approaches, simplifying the understanding of various algorithms by focusing on technologies, label data requirements, feature usage, and monitoring states. Lastly, by identifying gaps in current NILM research, this work sets a clear direction for future studies, discussing potential areas of innovation.

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Section: Yinyan Liumentioning

confidence: 99%

Section: ) Supervised Approachesmentioning

confidence: 99%

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Liu

Wang

You

2019

IEEE Trans. Smart Grid

163

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“…Although NILM has been widely implemented for the identification of conventional loads (e.g., [27], [28]), a low number of research studies have focused on DER at distribution levels. In [29], a disaggregation method for PV systems power generation from an IEEE 123-node standard test-feeder is developed. The approach evaluates the performance of machine learning algorithms including linear regression, random forest, and a multilayer perceptron.…”

Section: Overview Of Related Workmentioning

confidence: 99%

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

et al. 2023

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Increasing integration of distributed energy resources (DER) in the electrical network has led distribution network operators to unprecedented challenges. This issue is compounded by the lack of monitoring infrastructure on the low voltage (LV) side of distribution networks at residential and utility sides. Non-intrusive load monitoring (NILM) methods provide an opportunity to add value to conventional electric measurements and to increase the observability of LV networks for the implementation of active management network techniques and intelligent control of DER. This work proposes a novel implementation of NILM methods for the identification of DER electrical signatures from aggregated measurements taken at the LV side of a distribution transformer. The implementation evaluates three machine learning algorithms such as k Nearest Neighbours (kNN), random forest and a multilayer perceptron under 100 scenarios of DER integration. A year of minutely reported values of electric current, voltage, active power, and reactive power are used to train and test the proposed model. The F 1 scores achieved of 73% and 93% for Electrical Vehicles (EV) and rooftop photovoltaic (PV) respectively and processing times below 314 µs on an Intel Core i7-8700 machine. These results confirm the relevance of the NILM method based on low frequency electric measurements from the real-time identification of DER.INDEX TERMS Distributed energy resources (DER), non-intrusive load monitoring (NILM), low voltage networks distribution networks (LVN), random forest, supervised machine learning.The associate editor coordinating the review of this manuscript and approving it for publication was Wentao Fan .

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“…RF is another ML algorithm which operates as an ensemble of decision trees and often utilized for classification and regression tasks. Saeedi et al implemented different ML methods including decision tree, linear regression, and multilinear perceptron (MLP) to estimate PV generation output in Hawaii [74]. They claimed the superior performance by RF with highest R-squared value of 98% as compared to aforementioned ML models.…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

Kaur

Islam

Mahmud

et al. 2022

IET Generation Trans & Dist

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Energy forecasting plays a vital role in mitigating challenges in data rich smart grid (SG) systems involving various applications such as demand-side management, load shedding, and optimum dispatch. Managing efficient forecasting while ensuring the least possible prediction error is one of the main challenges posed in the grid today, considering the uncertainty in SG data. This paper presents a comprehensive and application-oriented review of state-of-the-art forecasting methods for SG systems along with recent developments in probabilistic deep learning (PDL). Traditional point forecasting methods including statistical, machine learning (ML), and deep learning (DL) are extensively investigated in terms of their applicability to energy forecasting. In addition, the significance of hybrid and data preprocessing techniques to support forecasting performance is also studied. A comparative case study using the Victorian electricity consumption in Australia and American electric power (AEP) datasets is conducted to analyze the performance of deterministic and probabilistic forecasting methods. The analysis demonstrates higher efficacy of DL methods with appropriate hyper-parameter tuning when sample sizes are larger and involve nonlinear patterns. Furthermore, PDL methods are found to achieve at least 60% lower prediction errors in comparison to other benchmark DL methods. However, the execution time increases significantly for PDL methods due to large sample space and a tradeoff between computational performance and forecasting accuracy needs to be maintained.

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An Adaptive Machine Learning Framework for Behind-the-Meter Load/PV Disaggregation

Cited by 37 publications

References 26 publications

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Non-Intrusive Load Monitoring by Voltage–Current Trajectory Enabled Transfer Learning

Distributed Energy Resources Electric Profile Identification in Low Voltage Networks Using Supervised Machine Learning Techniques

Energy forecasting in smart grid systems: recent advancements in probabilistic deep learning

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