Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network

Zang, Haixiang; Cheng, Lilin; Ding, Tao; Cheung, Kwok W.; Liang, Zhi Jie; Wei, Zhinong; Sun, Guoqiang

doi:10.1049/iet-gtd.2018.5847

Cited by 206 publications

(70 citation statements)

References 41 publications

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“…One of the biggest concerns connected with solar energy is its stochastic nature and variability, which threatens grid stability. A well-known approach to mitigate such uncertainty is the use of accurate forecasts [2].…”

Section: Introductionmentioning

confidence: 99%

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

et al. 2020

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The stochastic nature of renewable energy sources, especially solar PV output, has created uncertainties for the power sector. It threatens the stability of the power system and results in an inability to match power consumption and production. This paper presents a Convolutional Neural Network (CNN) approach consisting of different architectures, such as the regular CNN, multi-headed CNN, and CNN-LSTM (CNN-Long Short-Term Memory), which utilizes a sliding window data-level approach and other data pre-processing techniques to make accurate forecasts. The output of the solar panels is linked to input parameters such as irradiation, module temperature, ambient temperature, and windspeed. The benchmarking and accuracy metrics are calculated for 1 h, 1 day, and 1 week for the CNN based methods which are then compared with the results from the autoregressive moving average and multiple linear regression models in order to demonstrate its efficacy in making short-term and medium-term forecasts.

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Section: Introductionmentioning

confidence: 99%

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

et al. 2020

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“…Researchers fuse their understanding of different tasks into specific network structures rather than using a fixed shallow neural network structure. Different building blocks, including CNN and LSTM, make deep neural networks highly flexible and efficient. At present, researchers have proposed various techniques that can effectively train multilayer neural networks without leading to the disappearance of gradients or serious overfitting.…”

Section: Artificial Neural Network Approaches and Model For Short‐termentioning

confidence: 99%

An effective deep learning neural network model for short‐term load forecasting

Wang

et al. 2020

Concurrency and Computation

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Summary Energy load forecasting plays an important role in the smart grid, which can affect the promoting energy production and consumption decision‐making processes. In this paper, the state‐of‐the‐art deep learning (DL) neural models are used in the short‐term load forecasting, including the multilayer perceptron (MLP), the convolutional neural network (CNN), and the long short‐term memory (LSTM). A novel loss function is proposed for the load forecasting, and two commonly used benchmarks are used to verify the validity of the proposed function. The simulation results show that the mean absolute percentage error (MAPE) of the proposed loss function is 19.63% lower than cross‐entropy and 2.34% lower than mean absolute error (MAE). We compared the mentioned neural networks in different aspects, and the results show that in energy load forecasting, CNN has superior performance than MLP and LSTM in terms of high accuracy and robustness to weather changes.

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“…When the number was 100, the identification accuracy reached the maximum. The ReLU, a significant unsaturated activation function, was used as the activation function of the convolution layer, according to its successful application in CNN [44] and deep belief networks (DBN) [45]. Dropout is a valid way of resolving the over-fitting problem, but it plays a small role in the convolution layer and it was only adopted in the fully connected layer in this paper.…”

Section: Model Parameter Settingmentioning

confidence: 99%

Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

et al. 2019

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Power dispatching systems currently receive massive, complicated, and irregular monitoring alarms during their operation, which prevents the controllers from making accurate judgments on the alarm events that occur within a short period of time. In view of the current situation with the low efficiency of monitoring alarm information, this paper proposes a method based on natural language processing (NLP) and a hybrid model that combines long short-term memory (LSTM) and convolutional neural network (CNN) for the identification of grid monitoring alarm events. Firstly, the characteristics of the alarm information text were analyzed and induced and then preprocessed. Then, the monitoring alarm information was vectorized based on the Word2vec model. Finally, a monitoring alarm event identification model based on a combination of LSTM and CNN was established for the characteristics of the alarm information. The feasibility and effectiveness of the method in this paper were verified by comparison with multiple identification models.

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Hybrid method for short‐term photovoltaic power forecasting based on deep convolutional neural network

Cited by 206 publications

References 41 publications

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

Forecasting Solar PV Output Using Convolutional Neural Networks with a Sliding Window Algorithm

An effective deep learning neural network model for short‐term load forecasting

Identification Technology of Grid Monitoring Alarm Event Based on Natural Language Processing and Deep Learning in China

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