Two stage residential energy management under distribution locational marginal pricing

Mohsenzadeh, Amin; Pang, Chengzong

doi:10.1016/j.epsr.2017.09.010

Cited by 27 publications

(18 citation statements)

References 27 publications

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“…Regarding the UK-DALE dataset, the experiments have been performed by conducting a grid search within the following sets: [1 × 4, 1 × 16, 1 × 32] for the kernels dimension, [32,128] for the number of features maps, [2,4] for the pool size of the pooling layer, [512,4096] for the number of neurons in the dense layer. Moreover, 2 CNN layers have been adopted in both encoding and decoding stages: in the former stage, the number of feature maps of the second layer is twice the number of feature maps in the first layer, while it is the opposite for the decoder.…”

Section: Methodsmentioning

confidence: 99%

“…In the case of REDD dataset, after a preliminary evaluation, only one CNN layer has been taken into account, whereas the grid search has been performed over a wider set for each parameters with respect to the UK-DALE. Specifically, the set [1 × 8, 1 × 16, 1 × 32, 1 × 128] has been assumed for the kernels dimension, [8,16,32,128] for the number of features' maps, [1,2,4] for the pool size of the pooling layer, and [128,512,4096] for the number of neurons in the dense layer.…”

Section: Methodsmentioning

confidence: 99%

“…In the last several years, an increasing interest on the energy themes focused on the reduction of emissions from fossil sources has led the researchers to develop solutions oriented to improve the users' energy awareness in everyday life actions, as well as to assist them to better schedule these actions. Among these solutions, Non-Intrusive Load Monitoring (NILM) is surely one of the most studied [1], in addition to other useful ones, such as energy management and analytic [2,3], load task scheduling [4], or behaviour-based consumption [5]. These algorithms require high computational burden, since their results could be requested in real time to provide an immediate feedback to the user.…”

Section: Introductionmentioning

confidence: 99%

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A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Fagiani

Bonfigli²,

Principi

et al. 2019

Energies

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Nowadays, measurement systems strongly rely on the Internet of Things paradigm, and typically involve miniaturized devices on purpose. In these devices, the computational resources and signal acquisition rates are limited in order to preserve battery life. In addition, the amount of streamed data is affected by the network capacity strictly related to the transmission protocol constraints and the environmental conditions. All those limitations are in contrast with the need of exploiting all possible signal details for the task under study. In the specific application of interest, i.e., Non-Intrusive Load Monitoring (NILM), they could lead to low performance in the energy disaggregation process. To overcome these issues, an ad hoc data reduction policy needs to be adopted, in order to reduce the acquisition and elaboration burden of the device, and, at the same time, to ensure compliance with network bandwidth limits while maintaining a reliable signal representation. Moved by these motivations, an extended evaluation study concerning the application of data reduction strategy to the aggregate signal is presented in this work. In particular, a non-uniform subsampling (NUS) scheme is defined together with a uniform subsampling (US) strategy and compared, in terms of disaggregation performance, with the use of data at original sampling (OS) rate. A Deep Learning based technique is used for disaggregation, having the aggregate active power signal sampled according to diverse sampling schema mentioned above as input. The approaches are tested on the UK-DALE and REDD datasets, and the combination of US+NUS configurations allows for achieving a good performance in terms of F 1 -score, even superior than the one obtained with the OS rate, and a remarkable data reduction at the same time.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

Fagiani

Bonfigli²,

Principi

et al. 2019

Energies

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“…Hea = (1 − η e ) • η t • P t FC /η e (15) where η t denotes gas to thermal conversion efficiency. η e denotes gas to power conversion efficiency.…”

Section: Fc P Tmentioning

confidence: 99%

“…Finally, the optimal solution is found within these ten scenarios. Reference [15] models solar irradiance by Beta distribution and generates 1500 initial scenarios. The simultaneous backward reduction method [16] is employed to merge similar scenarios.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Home Energy Scheduling Strategy Under the Uncertainty in Photovoltaic Generations

Wang

Zhang

et al. 2020

IEEE Access

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To address the uncertainty in photovoltaic (PV) outputs for day-ahead home energy scheduling in hourly timescale, a novel stochastic optimization strategy based data-driven method is proposed. Based on available historical PV outputs, the Gaussian mixture model (GMM) algorithm combined with improved prediction strength of clustering method is applied to establish the forecasted probabilistic PV outputs model. Based on the seven-step approximation model of Gaussian distribution, only PV outputs with larger probability level at each hour are used to generate scenarios. Then the typical scenario set can be constructed by scenario reduction method. By finding the solution in typical scenario set using mixed-integer nonlinear programming (MINLP), the scheduling strategy will be closer to real cases. Test results verify the effectiveness of proposed probabilistic PV output model and solution method. INDEX TERMS Home energy scheduling, stochastic optimization, photovoltaic (PV) outputs, Gaussian mixture model (GMM), prediction strength of clustering method. NOMENCLATURE ABBREVIATION

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An Efficient Algorithm for Energy Management in Smart Grid for Various Improvements

Kumari,

Sharma

2024

Lecture Notes in Electrical Engineering

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Two stage residential energy management under distribution locational marginal pricing

Cited by 27 publications

References 27 publications

A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

A Non-Intrusive Load Monitoring Algorithm Based on Non-Uniform Sampling of Power Data and Deep Neural Networks

A Data-Driven Home Energy Scheduling Strategy Under the Uncertainty in Photovoltaic Generations

An Efficient Algorithm for Energy Management in Smart Grid for Various Improvements

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