Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

Zhu, Erxi; Pi, Dechang

doi:10.1155/2020/1929372

Cited by 6 publications

(4 citation statements)

References 24 publications

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“…In fact, ResNet is not the first model exploiting fast connection. Highway networks [35] and long and short-term memory network [36] units employ different gate structures to conduct fast connection.…”

Section: Use Of Residual Blocksmentioning

confidence: 99%

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Zhu

2021

Complexity

Self Cite

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Vehicle type recognition algorithms are broadly used in intelligent transportation, but the accuracy of the algorithms cannot meet the requirements of production application. For the high efficiency of the multilayer perceptive layer of Network in Network (NIN), the nonlinear features of local receptive field images can be extracted. Global average pooling (GAP) can avoid the network from overfitting, and small convolution kernel can decrease the dimensionality of the feature map, as well as downregulate the number of model training parameters. On that basis, the residual error is adopted to build a novel NIN model by altering the size and layout of the original convolution kernel of NIN. The feasibility of the algorithm is verified based on the Stanford Cars dataset. By properly setting weights and learning rates, the accuracy of the NIN model for vehicle type recognition reaches 97.2%.

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Section: Use Of Residual Blocksmentioning

confidence: 99%

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Zhu

2021

Complexity

Self Cite

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“…[9] uses principal component analysis (PCA) to reduce the dimension of historical data, and constructs a prediction model based on support vector machine (SVM) algorithm, which has high prediction accuracy. [10][11][12] established a photovoltaic power prediction model based on long short-term memory (LSTM) networks, which further improved the prediction accuracy and made it more applicable. However, the existing prediction methods mainly focus on centralized photovoltaic and rely on meteorological data.…”

Section: Introductionmentioning

confidence: 99%

Short-term power prediction of distributed photovoltaic based on CNN-LSTM and meteorological interpolation

Chen,

Su,

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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The photovoltaic power generation has strong randomness and fluctuation. Considering the traditional photovoltaic power prediction methods mainly focus on centralized photovoltaic and rely heavily on meteorological data, while distributed photovoltaic usually does not have perfect meteorological data, so the existing photovoltaic power prediction methods are difficult to apply to distributed photovoltaic. Based on the known wide-area meteorological resource data, the model using the Kriging method and the inverse distance weighting (IDW) method to interpolate the meteorological data of the of the distributed photovoltaic power stations by its longitude and latitude positions, using the CNN network to extract the hidden feature information, and constructs a prediction model based on LSTM. The model verifies the effectiveness of the method through actual data.

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“…To work out these events, exploring and exploiting renewable energy worldwide should be the ultimate focus of attention (Islam, 2017;Shezan et al, 2017;Liu et al, 2020;Elavarasan et al, 2021). Photovoltaic (PV) power, which is unlimited, green, and available, has become a key point in new energy resource research (Jithin and Roykumar, 2018;Shelat et al, 2019;Zhu and Pi, 2020;Tan et al, 2021). The International Energy Agency announced that, until 2019, the global accumulative installed capacity of PV power exceeded 627 GW.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Forecasting Frame Based on Deep Learning and Multi-Objective Optimization for Planning Solar Energy Management: A Case Study

Liu¹,

Li²,

Zhou³

2021

Front. Energy Res.

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There are many prediction models that have been adopted to predict uncertain and non-linear photovoltaic power time series. Nonetheless, most models neglected the validity of data preprocessing and ensemble learning strategies, which leads to low forecasting precision and low stability of photovoltaic power. To effectively enhance photovoltaic power forecasting accuracy and stability, an ensemble forecasting frame based on the data pretreatment technology, multi-objective optimization algorithm, statistical method, and deep learning methods is developed. The proposed forecasting frame successfully integrates the advantages of multiple algorithms and validly depict the linear and nonlinear characteristic of photovoltaic power time series, which is conductive to achieving accurate and stable photovoltaic power forecasting results. Three datasets of 15-min photovoltaic power output data obtained from different time periods in Belgium were employed to verify the validity of the proposed system. The simulation results prove that the proposed forecasting frame positively surpasses all comparative hybrid models, ensemble models, and classical models in terms of prediction accuracy and stabilization. For one-, two-, and three-step predictions, the MAPE values obtained from the proposed frame were less than 2, 3, and 5%, respectively. Discussion results also verify that the proposed forecasting frame is obviously different from other comparative models, and is more stable and high-efficiency. Thus, the proposed frame is highly serviceable in elevating photovoltaic power forecasting performance and can be used as an efficient instrument for intelligent grid programming.

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Photovoltaic Generation Prediction of CCIPCA Combined with LSTM

Cited by 6 publications

References 24 publications

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Vehicle Type Recognition Algorithm Based on Improved Network in Network

Short-term power prediction of distributed photovoltaic based on CNN-LSTM and meteorological interpolation

Ensemble Forecasting Frame Based on Deep Learning and Multi-Objective Optimization for Planning Solar Energy Management: A Case Study

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