Abstract
In order to remedy problems encompassing large-scale data being collected by photovoltaic (PV) stations, multiple dimensions of power prediction mode input, noise, slow model convergence speed, and poor precision, a power prediction model that combines the Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) with Long Short-Term Memory (LSTM) network was proposed in this study. The corresponding model uses factor correlation coefficient to evaluate the factors that affect PV generation and obtains the most critical factor of PV generation. Then, it uses CCIPCA to reduce the dimension of PV super large-scale data to the factor dimension, avoiding the complex calculation of covariance matrix of algorithms such as Principal Component Analysis (PCA) and to some extent eliminating the influence of noise made by PV generation data acquisition equipment and transmission equipment such as sensors. The training speed and convergence speed of LSTM are improved by the dimension-reduced data. The PV generation data of a certain power station over a period is collected from SolarGIS as sample data. The model is compared with Markov chain power generation prediction model and GA-BP power generation prediction model. The experimental results indicate that the generation prediction error of the model is less than 3%.
Highlights
With the gradual implementation of the “Internet + energy” policy, the PV generation industry is rapidly developing. e proposed incorporation of artificial intelligence has instilled new incentives for the PV generation industry
Neural networks are widely used in PV generation prediction, and a number of power generation prediction models have surfaced to this effect, including neural networks based on time series [4], GA-BP neural network [5], deep belief network [6], and fuzzy neural network [7]
principal component analysis (PCA) must input all sample data before starting the analysis, which does not align with the objectives of big data; the Candid Covariance-free Incremental Principal Component Analysis (CCIPCA) method was proposed
Summary
With the gradual implementation of the “Internet + energy” policy, the PV generation industry is rapidly developing. e proposed incorporation of artificial intelligence has instilled new incentives for the PV generation industry. Neural networks are widely used in PV generation prediction, and a number of power generation prediction models have surfaced to this effect, including neural networks based on time series [4], GA-BP neural network [5], deep belief network [6], and fuzzy neural network [7]. Such models find it difficult to overcome issues in convergence and local optimization, and they suffer from large prediction errors, mainly because many network input compositions are present, along with noises in the sample data. Combined with LSTM [9], a power generation prediction model was established to overcome problems such as local convergence and slow training speed, controlling the prediction error of PV generation within 3%
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