A novel network training approach for solving sample imbalance problem in Photovoltaic power prediction

Randomness and intermittency are crucial challenges in photovoltaic (PV) power prediction. Most studies concentrate on addressing the randomness of PV power, and tend to overlook the intermittency that leads to sample imbalance, which negatively affects prediction accuracy. To address the sample imbalance, a novel approach called segment imbalance regression (SIR) is proposed. The SIR method proactively exploits the inherent imbalanced nature of samples by investigating the interactions among neighbouring samples, which leads to dynamical assigning weights. Through focused training and segmental prediction, SIR selectively retains the outside information while focusing segment inside, which enhances the gradient descent process and ultimately leads to improved training performance. With crisscross optimization (CSO), SIR demonstrates its performance sufficiently with an average RMSE reduction of 21.17% and 40.76% in the multi-step prediction and day-ahead prediction cases, respectively.