Deep recurrent extreme learning machine for behind-the-meter photovoltaic disaggregation

Saffari, Mohsen; Khodayar, Mahdi; Khodayar, Mohammad E.

doi:10.1016/j.tej.2022.107137

Cited by 8 publications

(2 citation statements)

References 46 publications

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“…The smart meters used by PV customers cannot separate the PV power from the net load data [10, 19]. In [20, 21] by extracting the historical net load power characteristics to predicted the actual load power and PV power of the user side. At moment t, the net load power be

P_t^{NL}

, the actual load power be

P_t^L

, and the BTM PV generation power be

P_t^{PV}

, net load power is equal to the actual load power minus the BTM PV output power, as shown in ().…”

Section: Description Of Btm Pv Powermentioning

confidence: 99%

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Zhang,

Hong,

Ibrahim

et al. 2023

IET Renewable Power Gen

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Distributed photovoltaic (PV) systems often lack adequate measurements due to cost considerations, which makes it very difficult to predict them accurately. Here, an approach is proposed for behind‐the‐meter (BTM) PV power prediction using attention‐LSTM neural network and transfer learning. First, the weather is classified into four types based on the deviation ratio β. Second, the correlation analysis algorithm identifies the weather factors that contribute the most to PV power generation as GHI, DNI, humidity, and temperature. Then, attention‐LSTM pre‐trained model is constructed, in which the LSTM network fully extracts the temporal characteristics of PV power generation, while the attention mechanism enhances the attention to the important information in the input. Finally, a novel BTM PV short‐term power prediction method using transfer learning that the upper layer parameters of the pre‐trained model were frozen, and a small amount of DP‐NB data was used to fine‐tune the model. The proposed methodology has higher prediction accuracy compared with other benchmark methods under four weather types, and the time cost is saved by freezing the parameters of the first layer of the network by 37.2%.

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P_t^{NL}

, the actual load power be

P_t^L

, and the BTM PV generation power be

P_t^{PV}

, net load power is equal to the actual load power minus the BTM PV output power, as shown in ().…”

Section: Description Of Btm Pv Powermentioning

confidence: 99%

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Zhang,

Hong,

Ibrahim

et al. 2023

IET Renewable Power Gen

View full text Add to dashboard Cite

show abstract

“…Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) that are designed to handle the vanishing and exploding gradient problems that can occur in traditional RNNs [121,122]. LSTMs use a combination of memory cells and gating mechanisms to selectively remember or forget information from previous time steps, allowing them to capture long-term dependencies in sequential data.…”

Section: Long Short-term Memory Networkmentioning

confidence: 99%

Deep Neural Networks in Power Systems: A Review

Khodayar

Regan

2023

Energies

Self Cite

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Identifying statistical trends for a wide range of practical power system applications, including sustainable energy forecasting, demand response, energy decomposition, and state estimation, is regarded as a significant task given the rapid expansion of power system measurements in terms of scale and complexity. In the last decade, deep learning has arisen as a new kind of artificial intelligence technique that expresses power grid datasets via an extensive hypothesis space, resulting in an outstanding performance in comparison with the majority of recent algorithms. This paper investigates the theoretical benefits of deep data representation in the study of power networks. We examine deep learning techniques described and deployed in a variety of supervised, unsupervised, and reinforcement learning scenarios. We explore different scenarios in which discriminative deep frameworks, such as Stacked Autoencoder networks and Convolution Networks, and generative deep architectures, including Deep Belief Networks and Variational Autoencoders, solve problems. This study’s empirical and theoretical evaluation of deep learning encourages long-term studies on improving this modern category of methods to accomplish substantial advancements in the future of electrical systems.

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A hybrid data-driven online solar energy disaggregation system from the grid supply point

Zhang

Kuenzel

Guo

et al. 2022

Complex Intell. Syst.

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The integration of small-scale Photovoltaics (PV) systems (such as rooftop PVs) decreases the visibility of power systems, since the real demand load is masked. Most rooftop systems are behind the metre and cannot be measured by household smart meters. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by rooftop PV systems and the ground truth demand load from net measurements. A 1D Convolutional Neural Network (CNN) Bidirectional Long Short-Term Memory (BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (Advanced Metering Infrastructure (AMI) data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate PV generation, and the system also enables online and offline modes. The effectiveness of the proposed algorithm is evaluated by comparing it to baselines. The results show that the proposed method achieves good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify that the proposed system has good robustness and can be applied to other feeders.

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Deep recurrent extreme learning machine for behind-the-meter photovoltaic disaggregation

Cited by 8 publications

References 46 publications

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Short‐term prediction of behind‐the‐meter PV power based on attention‐LSTM and transfer learning

Deep Neural Networks in Power Systems: A Review

A hybrid data-driven online solar energy disaggregation system from the grid supply point

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