Photovoltaic generation power prediction research based on high quality context ontology and gated recurrent neural network

Liu, Hongfei; Gao, Qian; Ma, Pengcheng

doi:10.1016/j.seta.2021.101191

Cited by 13 publications

(10 citation statements)

References 18 publications

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“…With the deepening of digital campus construction and the development of Internet technology, a large number of educational data have been accumulated, but they cannot be used reasonably [4]. Therefore, this paper takes the data of college students accumulated in the digital campus as the research object and, based on the neural network research method [5,6], explores the hidden personal-ized information of students and predicts the future employment destination to provide scientific and technological support for college education [7]. In employment recommendation and prediction, some methods have poor prediction effect and the recommendation results do not match the demand.…”

Section: Introductionmentioning

confidence: 99%

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

Cheng

2022

Wireless Communications and Mobile Computing

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This paper takes the data of a university graduate as the research object. By consulting various literatures and literature analyses, we can understand the impact of students’ academic performance, English level, and other activities on students’ employment development and select data on this basis. The collected data is cleaned, integrated, and transformed to form a standard data set. The factors affecting graduates’ employment are complex and diverse, with high data feature dimensions, sparse links between features, complex and diverse attributes, and both discrete and continuous features. According to the characteristics of college students’ employment data, this paper uses the deep-seated neural network with strong learning ability and adaptability to predict college students’ employment, so as to provide guidance for college students’ employment. Firstly, based on deep learning and Feedforward Neural Network technology, a prediction model of college students’ employment destination with six influencing factors is established, and the prediction accuracy of the model is evaluated. The ACC value and loss value are used as indicators to test whether the prediction effect of the prediction model is good. The results show that the prediction effect of the model is worthy of further research and optimization. Finally, combined with the actual data of graduates, the practical application of the prediction model is analyzed. Compared with the traditional machine learning algorithm, the effectiveness of the algorithm is verified.

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Section: Introductionmentioning

confidence: 99%

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

Cheng

2022

Wireless Communications and Mobile Computing

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“…This paper concerns 1 week ahead of daily peak load forecasting (MTLF). Load forecasting methods can be classified into statistical methods [1][2][3] and machine learning methods [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Recently, deep learning algorithms have also received much attention [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Load forecasting methods can be classified into statistical methods [1][2][3] and machine learning methods [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. Recently, deep learning algorithms have also received much attention [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

“…Deep learning neural networks (DLNNs) are more advanced, efficient and complex methods than traditional MLNN and SVM. DLNNs have many applications, such as non-intrusive load transient identification [12], locational marginal price forecasting [13], photovoltaic generation power prediction [14] and short-term wind-speed forecasting [15]. Yu et al proposed a bespoke gated recurrent neural network (RNN) that incorporated dynamic time warping (DTW) for daily peak load forecasting; they used shape-based DTW distance to match the most similar load curve [16].…”

Section: Introductionmentioning

confidence: 99%

“…4. The topology of an MLNN or DLNN is not explored in [4][5][6][7][8][9][12][13][14][15][16][17][18][19]. It is obtained by trial-and-error.…”

Section: Introductionmentioning

confidence: 99%

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Week‐ahead daily peak load forecasting using genetic algorithm‐based hybrid convolutional neural network

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Cheng

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IET Generation Trans & Dist

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Daily peak load forecasting is crucial for the operation of bulk power systems, including economic dispatch and unit commitment. It is also essential for peak load shaving and load management in distribution systems. In power markets, peak load forecasting helps participants develop bidding strategies. This paper proposes a new method, using a hybrid convolutional neural network (CNN) that is cascaded with a fully-connected network, for making week-ahead daily peak load forecasts. The proposed method uses three loops to obtain the optimal CNN: The outer loop performs crossover/mutation operations and tournament selection to produce chromosomes to optimize the network topology and hyperparameters (such as kernel size) of the hybrid CNN by genetic algorithms; the middle loop deals with the order of chromosomes; the inner loop optimizes the synaptic weights and parameters (e.g. values of a kernel) using Adam optimizer. Daily peak load data and corresponding meteorological data for Taiwan are explored. Simulation results show that the proposed method outperforms the traditional CNN, multi-layer neural network, recurrent neural network, support vector regression and vector autoregressive moving average model.

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Intelligent Tools and Techniques for Renewable Energy Analytics: A Review on PV Generation Prediction

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Tomar

2022

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Photovoltaic generation power prediction research based on high quality context ontology and gated recurrent neural network

Cited by 13 publications

References 18 publications

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

Employment Data Screening and Destination Prediction of College Students Based on Deep Learning

Week‐ahead daily peak load forecasting using genetic algorithm‐based hybrid convolutional neural network

Intelligent Tools and Techniques for Renewable Energy Analytics: A Review on PV Generation Prediction

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