Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information

Zhang, Hao; Niu, Dongxiao; Wang, Keke; Shi, Yucheng; Ji, Zhengsen; Xu, Xiaomin

doi:10.1016/j.energy.2021.120908

Cited by 86 publications

(18 citation statements)

References 41 publications

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“…Literature 1 (Yin et al, 2020) Extreme learning machine, non-iterative correction theory, seasonal model 2020 Literature 2 (Niu et al, 2020) Random forest feature selection, complete ensemble empirical mode decomposition, backpropagation, particle swarm optimization 2020 Literature 3 (Zhang et al, 2020a) Dendritic neural network, wavelet transform algorithm 2020 Literature 4 (Li et al, 2020) Wavelet packet decomposition, LSTM 2020 Literature 5 (Agga et al, 2021) CNN, LSTM, ConvLSTM 2021 Literature 6 (Mellit et al, 2021) LSTM, Bidirectional LSTM, GRU, Bidirectional GRU, CNN, CNN-LSTM, CNN-GRU 2021 Literature 7 (Luo et al, 2021) Pearson correlation coefficient, LSTM, physical constraints 2021 Literature 8 (Zhen et al, 2021) Genetic algorithm, Bidirectional LSTM 2021 Literature 9 (Qu et al, 2021a) CNN, LSTM, CNN-LSTM 2021 etc.…”

Section: Literature Methods Of Construction Yearmentioning

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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Section: Literature Methods Of Construction Yearmentioning

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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“…The unidirectional GRU model uses previous information to predict follow-up information, while the bidirectional GRU can comprehensively learn the time-correlated information in both forward and backward directions simultaneously to improve prediction accuracy [36]. The Bi-GRU extends the unidirectional GRU by introducing a second layer, as shown in Figure 4.…”

Section: Establishment Of the Slope Displacement Prediction Modelmentioning

confidence: 99%

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

et al. 2022

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Predicting slope deformation prediction is crucial for early warning of slope failure, preventing damage to properties, and saving human lives. However, in practice, equipment maintenance causes discontinuity in the displacement data, and the traditional prediction models based on deep networks do not perform well in this case. To solve the problem of prediction accuracy in case of discontinuous and inadequate data, we propose a combined displacement prediction model that integrates the bidirectional gated recurrent unit (Bi-GRU), attention mechanism, and transfer learning. The Bi-GRU is employed to extract the forward and backward characteristics of displacement series, and the attention mechanism is utilized to give different weights to the extracted information so as to highlight the critical information. Transfer learning is used to guarantee prediction accuracy in case of discontinuous and limited data. The model is then employed to predict the slope displacement of the JinYu Cement Plant in China. Finally, the modeling results excellently agree with measured displacement, especially in case of insufficient sample data.

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“…These investigators primarily used classical optimization approaches to achieve the forecasting performance of SVM by optimizing the parameter values. The optimized genetic algorithm has been proposed to optimize Bidirectional Long Short-Term memory (BiLSTM) to forecast multiple photovoltaic power outputs [16]. The results demonstrate the importance of adjacent photovoltaic system power series, and the suggested model performs adequately in ultra-short-term forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

2021

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In this research paper, a nonlinear autoregressive with exogenous input (NARX) model of the nonlinear system based on neural network and time series analysis is proposed to deal with the one-month forecast of the produced power from photovoltaic modules (PVM). The PVM is a monocrystalline cell with a rated production of 175 watts that is placed at Heliopolis University, Bilbéis city, Egypt. The NARX model is considered powerful enough to emulate the nonlinear dynamic state-space model. It is extensively performed to resolve a variety of problems and is mainly important in complex process control. Moreover, the NARX method is selected because of its quick learning and completion times, as well as high appropriateness, and is distinguished by advantageous dynamics and interference resistance. The neural network (NN) is trained and optimized with three algorithms, the Levenberg–Marquardt Algorithm (NARX-LMA), the Bayesian Regularization Algorithm (NARX-BRA) and the Scaled Conjugate Gradient Algorithm (NARX-SCGA), to attain the best performance. The forecasted results using the NARX method based on the three algorithms are compared with experimentally measured data. The NARX-LMA, NARX-BRA and NARX-SCGA models are validated using statistical criteria. In general, weather conditions have a significant impact on the execution and quality of the results.

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Photovoltaic power forecasting based on GA improved Bi-LSTM in microgrid without meteorological information

Cited by 86 publications

References 41 publications

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

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

Landslide Displacement Prediction Based on Transfer Learning and Bi-GRU

Forecasting of Photovoltaic Power by Means of Non-Linear Auto-Regressive Exogenous Artificial Neural Network and Time Series Analysis

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