AdaBoost$^{+}$: An Ensemble Learning Approach for Estimating Weather-Related Outages in Distribution Systems

Kankanala, Padmavathy; Das, Sanjoy; Pahwa, Anil

doi:10.1109/tpwrs.2013.2281137

Cited by 79 publications

(40 citation statements)

References 33 publications

(46 reference statements)

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“…EL has currently been only preliminarily applied in Smart EEPS represented by the fields of SG and EI. The applied scenarios include generation‐consumption coordinated frequency control, estimating weather‐related outages in distribution systems, power system security assessment, short‐term forecasting of the PV output power, short‐term electricity load forecasting, energy consumption forecasting (such as nuclear energy and hydropower), short‐term wind power ramp forecasting, and predicting overall solar cell power conversion efficiency …”

Section: Ensemble Learningmentioning

confidence: 99%

“…In addition, aiming at the major environment factors such as animals, trees, and weather that easily cause power outages in the electric utility distribution systems, a novel EL approach was developed in Kankanala et al based on a boosting algorithm, which is called AdaBoost + . This EL‐based model has the potential to reduce power grid operating costs and customer power outages; thus, it was mainly used to estimate weather‐caused power outages.…”

Section: Ensemble Learningmentioning

confidence: 99%

“…126 EL has currently been only preliminarily applied in Smart EEPS represented by the fields of SG and EI. The applied scenarios include generation-consumption coordinated frequency control, 103 estimating weatherrelated outages in distribution systems, 127 power system security assessment, 128 short-term forecasting of the PV output power, 129 short-term electricity load forecasting, 130 energy consumption forecasting (such as nuclear energy 131 and hydropower 132 ), short-term wind power ramp forecasting, 133 and predicting overall solar cell power conversion efficiency. 134 Notably, EL techniques have mainly been applied to frequency control, power system security and stability assessment, and load forecasting in the field of SG, while in the EI field, EL techniques have primarily been used for energy consumption forecasting (eg, renewables, nuclear energy, and hydropower consumption forecasting) and power generation forecasting.…”

Section: Research Status Of El In Smart Eepsmentioning

confidence: 99%

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A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Cheng

2019

Int J Energy Res

216

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Summary The new generation of artificial intelligence (AI), called AI 2.0, has recently become a research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and electric power system (Smart EEPS). In AI 2.0, machine learning (ML) forms a typical representative algorithm category used to achieve predictions and judgments by analyzing and learning from massive amounts of historical and synthetic data to help people make optimal decisions. ML has preliminarily been applied to the Smart Grid (SG) and Energy Internet (EI) fields, which are important Smart EEPS representatives. AI 2.0, especially ML, is undergoing a critical period of rapid development worldwide and will play an essential role in Smart EEPS. In this context, this study, combined with the emerging SG and EI technologies, takes the typical representative of AI 2.0—ML—as the research objective and reviews its research status in the operation, optimization, control, dispatching, and management of SG and EI. The paper focuses on introducing and summarizing the mainstream uses of seven representative ML methods, including reinforcement learning, deep learning, transfer learning, parallel learning, hybrid learning, adversarial learning, and ensemble learning, in the SG and EI fields. In this survey, we begin with an introduction to these seven types of ML methods and then systematically review their applications in Smart EEPS. Finally, we discuss ML development under the big data thinking and offer a prospect for the future development of AI 2.0 and ML in Smart EEPS. We conduct this survey intended to arouse the interest and excitement of experts and scholars in the EEPS industry and to look ahead to efforts that jointly promote the rapid development of AI 2.0 in the Smart EEPS field.

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Section: Ensemble Learningmentioning

confidence: 99%

Section: Ensemble Learningmentioning

confidence: 99%

Section: Research Status Of El In Smart Eepsmentioning

confidence: 99%

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A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

Cheng

2019

Int J Energy Res

216

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“…It is an event detection technique that outputs a discrete or binary result. It is based on an AdaBoost machine learning algorithm that operates depending on its dataset and trainings, which combines a series of weak classifiers into a final boosted output [14]. A total of 29 minutes training videos based on 435 GB of 30 fps, 1080p uncompressed Red Green Blue (RGB) and 424p depth data were recorded and stored as a training dataset.…”

Section: B Machine Learning Approachmentioning

confidence: 99%

A comparison between heuristic and machine learning techniques in fall detection using Kinect v2

Amini

Banitsas

Cosmas

2016

2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Abstract-In this paper, two algorithms were tested on 11 healthy adults: one based on heuristic and another one on video tagging machine learning methods for automatic fall detection; both utilizing Microsoft Kinect v2. For our heuristic approach, we used skeletal data to detect falls based on a set of instructions and signal filtering methods. For the machine learning approach, we implemented a dataset utilizing the Adaptive Boosting Trigger (AdaBoostTrigger) algorithm via video tagging to enable fall detection. For each approach, each subject on average has performed six true positive and six false positive fall incidents in two different conditions: one with objects partially blocking the sensor's view and one with partial obstructed field of view. The accuracy of each approach has been compared against one another in different conditions. The result showed an average of 95.42 % accuracy in the heuristic approach and 88.33 % in machine learning technique. We conclude that heuristic approach performs more accurately for fall detection when there is a limited number of training dataset available. Nevertheless, as the gesture detection's complexity increases, the need for a machine learning technique is inevitable.

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“…Events during storms are naturally stochastic [13][14][15][16]. When assessing system reliability, there are many works that use only two weather state representations: a normal-weather stage and an adverse-weather stage.…”

Section: Introductionmentioning

confidence: 99%

Automation Effects on Reliability and Operation Costs in Storm Restoration

Cheng

Önen

Zhu

et al. 2015

Electric Power Components and Systems

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AdaBoost$^{+}$: An Ensemble Learning Approach for Estimating Weather-Related Outages in Distribution Systems

Cited by 79 publications

References 33 publications

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems

A comparison between heuristic and machine learning techniques in fall detection using Kinect v2

Automation Effects on Reliability and Operation Costs in Storm Restoration

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