Abstract
Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors. A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer. The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features. Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer. Bayesian optimization is used to select the optimal combined features. The effectiveness of the proposed model is verified on two actual PV power plants in one area of China. The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model.
Highlights
With the global concern about environmental issues, it has become the consensus of the world to develop renewable energy resources, such as wind [1], hydro [2], fuel cell [3], photovoltaic (PV) [4], [5]
We can conclude that the proposed prediction model can improve the prediction accuracy of PV power output and can reflect the tendency of the PV power generation at sunny weather
PV power output is strongly related to the meteorological factors, and it shows the intermittency and volatility
Summary
With the global concern about environmental issues, it has become the consensus of the world to develop renewable energy resources, such as wind [1], hydro [2], fuel cell [3], photovoltaic (PV) [4], [5]. LSTM [28] and LSTM-based self-encoder [29] perform prediction directly without considering any other additional characteristics; with deep belief networks used in [30], the factors that affect PV power output have been analyzed, and the relevant meteorological characteristics are used, but the characteristics are not further explored. In [31], restricted Boltzmann machine is proposed, and only original characteristics related to PV power output are explored with only temperature characteristics included, and no more weather information are used; wavelet-based decomposing CNN-LSTM [32], [33] only uses direct prediction method, and no more additional information has been explored; and Bayesian deep learning model [34] is a method of uncertainty prediction. 4) Time features The time features, such as the month of the year, the day of the week, the day of the month, the hour of the day, and the minute of the hour are extracted
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