Abstract: Improving the accuracy of photovoltaic power prediction
is crucial for grid scheduling planning and is essential for the safe,
stable, and economic operation of power systems. Based on the
statistical characterization of the data, a two-stage PV power
prediction model with error correction is developed. First, an Elman
neural network model optimized by a small habitat genetic algorithm is
introduced; subsequently, a more accurate model for the preliminary
prediction error probability distribution is established, based on its
distribution characteristics. This model aims to achieve error
correction of the preliminary prediction results. The empirical results,
derived from actual PV power curves and meteorological data, demonstrate
the effectiveness of the proposed method.
Introduction: With the advancement of technology and reduction in
costs, photovoltaic (PV) power generation has emerged as an important
component of the renewable energy sector [1]. However, the power
output from PV systems is significantly influenced by environmental
factors, rendering its prediction highly uncertain and thereby impacting
the stability and dispatch efficiency of the power grid. Current
prediction methods comprise physical models [2], statistical
analysis [3], and machine learning, with the latter gaining
prominence in this field due to its ability to handle complex data and
resist environmental disturbances [4]. The advantage of machine
learning methods lies in their capability to achieve more reliable grid
operation and efficient energy utilization through refined prediction
methods. Strategies to improve the predictive performance of machine
learning models include data preprocessing such as data cleaning and
standardization [5], feature engineering [6], selecting
appropriate models and optimizing hyperparameters [7, 8], and
applying ensemble learning methods. For deep learning, this also
includes adjusting network architecture, applying regularization
techniques, and tuning the learning rate [9]. These strategies work
together to enhance the model’s prediction accuracy and generalization
ability. Despite significant research achievements in the field of PV
power prediction, exploring new methods to further enhance predictive
capability remains of great research value. This paper proposes a
two-stage PV power prediction model aimed at further improving the
accuracy of PV power generation predictions by combining advanced
machine learning techniques with error correction mechanisms. Empirical
analysis shows that this model brings an innovative research method to
the field of PV power prediction, demonstrating its potential in
improving prediction accuracy.
Adaptive Genetic Algorithm: Genetic algorithms derive their
foundation from the core concepts of Darwin’s theory of evolution. They
simulate real-world problems on a computer, mimicking the process of
chromosome selection, crossover, and mutation in biological evolution,
as illustrated in Figure 1.