Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting

Ren, Xiaoying; Zhang, Fei; Zhu, Hongguang; Liu, Yongqian

doi:10.1016/j.apenergy.2022.119682

Cited by 32 publications

(14 citation statements)

References 35 publications

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“…However, the model is highly dependent on the granularity used in the training set. In the work of [11], a CNN with four inputs of different kernel sizes is proposed to support more input granularities, however the receptive field of each one is static and limits the ability to acquire different representations of granularity variations. In [12], an unsupervised framework called Temporal Neighborhood Coding (TNC) is proposed for learning multivariate and non-stationary complex time series representations.…”

Section: A Related Workmentioning

confidence: 99%

A Granularity Invariant Method for the Classification of Energy Production Time Series in Photovoltaic Plants

Centeno¹,

García-Ordás²,

García-Olalla³

et al. 2023

IEEE Access

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The treatment of photovoltaic power production time series often faces the challenge of unifying the granularity of the series when generating a predictive model. This can limit the generation of a dataset in terms of the time covered and the number of examples. In addition, models built with data of static granularities tend to show rigidity when facing granularity variations, invalidating them for scenarios different from those of the data they were trained on. To address this issue, this paper presents a novel method specifically indicated for Deep-Learning models that shows invariance to granularity called Synthesis. This operation can be added as a layer to an artificial neural network, allowing it to be applied to any power production time series and synthesizing the content of an arbitrarily long time series into a fixed-size vector which can be used for classification or regression regardless of the initial time series length. The experiments with the NIST Campus Photovoltaic dataset demonstrate the effectiveness of the method, showing an F1-Score of 1.0 for the classification of series with granularities between 2 minutes and 2 hours, and an F-Score of 1.0 for the classification of time series with variations of granularity throughout time when training with 5-minute granularity samples.

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Section: A Related Workmentioning

confidence: 99%

A Granularity Invariant Method for the Classification of Energy Production Time Series in Photovoltaic Plants

Centeno¹,

García-Ordás²,

García-Olalla³

et al. 2023

IEEE Access

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“…The study [ 64 ] reveals that the output power with the insolation and the air temperature has a linear and nonlinear correlation, correspondingly. Recently, researchers have been more interested in the ML application to increase the accuracy of the forecasters [ 61 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 ].…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

“…The simple (in [ 61 ], preprocessing generated normalized insolation; in [ 73 ], preprocessing elaborated k-means) and complex data preprocessing algorithms (in [ 71 ], four CNNs with different filters mine simple features from a sequence of time series; a single-kernel CNN mines the meta features from the simple features) provide for the ML model better performance ( Table 7 ).…”

Section: Machine Learning Applications For a Solar Plant Systemmentioning

confidence: 99%

A Review on Machine Learning Applications for Solar Plants

Engel

2022

Sensors

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A solar plant system has complex nonlinear dynamics with uncertainties due to variations in system parameters and insolation. Thereby, it is difficult to approximate these complex dynamics with conventional algorithms whereas Machine Learning (ML) methods yield the essential performance required. ML models are key units in recent sensor systems for solar plant design, forecasting, maintenance, and control to provide the best safety, reliability, robustness, and performance as compared to classical methods which are usually employed in the hardware and software of solar plants. Considering this, the goal of our paper is to explore and analyze ML technologies and their advantages and shortcomings as compared to classical methods for the design, forecasting, maintenance, and control of solar plants. In contrast with other review articles, our research briefly summarizes our intelligent, self-adaptive models for sizing, forecasting, maintenance, and control of a solar plant; sets benchmarks for performance comparison of the reviewed ML models for a solar plant’s system; proposes a simple but effective integration scheme of an ML sensor solar plant system’s implementation and outlines its future digital transformation into a smart solar plant based on the integrated cutting-edge technologies; and estimates the impact of ML technologies based on the proposed scheme on a solar plant value chain.

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“…In ref. [17], for the challenge of predicting PV power generation within hours, a quad-kernel CNN model (QK_ CNN) was employed. This model replaced the first FC layer in the traditional CNN with a global maximum pooling strategy, which reduced the amount of calculations, avoided overfitting, and reduced the dimensionality to extract global salient features.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

Gao,

Wang,

Guo

et al. 2024

Sustainability

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To ensure high-quality electricity, improve the dependability of power systems, reduce carbon emissions, and promote the sustainable development of clean energy, short-term photovoltaic (PV) power prediction is crucial. However, PV power is highly stochastic and volatile, making accurate predictions of PV power very difficult. To address this challenging prediction problem, in this paper, a novel method to predict the short-term PV power using a nonlinear spiking neural P system-based ESN model has been proposed. First, we combine a nonlinear spiking neural P (NSNP) system with a neural-like computational model, enabling it to effectively capture the complex nonlinear trends in PV sequences. Furthermore, an NSNP system featuring a layer is designed. Input weights and NSNP reservoir weights are randomly initialized in the proposed model, while the output weights are trained by the Ridge Regression algorithm, which is motivated by the learning mechanism of echo state networks (ESNs), providing the model with an adaptability to complex nonlinear trends in PV sequences and granting it greater flexibility. Three case studies are conducted on real datasets from Alice Springs, Australia, comparing the proposed model with 11 baseline models. The outcomes of the experiments exhibit that the model performs well in tasks of PV power prediction.

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Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting

Cited by 32 publications

References 35 publications

A Granularity Invariant Method for the Classification of Energy Production Time Series in Photovoltaic Plants

A Granularity Invariant Method for the Classification of Energy Production Time Series in Photovoltaic Plants

A Review on Machine Learning Applications for Solar Plants

Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems

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