Considering the limited availability of meteorological data in practice, this paper investigates the short-term photovoltaic output prediction problem based on data decomposition and error correction to further improve prediction accuracy. Firstly, according to the analysis of the variation characteristics of photovoltaic output data, the Seasonal and Trend decomposition using Loess (STL) method is used to decompose the original data into three components: seasonal term, trend term, and residual term. Considering that the variation patterns of different components are different, based on the division of the dataset, temporal convolutional network (TCN)-based prediction models for each component are constructed separately, and the prediction results are superimposed to obtain the predicted value of the photovoltaic output. Secondly, an error dataset is constructed based on the prediction errors of the training set and validation set, and a TCN error prediction model is established. The error prediction value is used as compensation to correct the photovoltaic output prediction value, and the final photovoltaic output prediction value is obtained. Finally, based on the measured photovoltaic output data of a certain region in China, the effectiveness and advancement of the proposed method are demonstrated through the ablation and comparative experiments.

Probabilistic voltage assessment under multiple contingencies involves numerous network topologies and uncertain operating conditions, resulting in substantial surrogate modeling and computational costs. To address this problem, this paper proposes a voltage-increment-based low-rank (LR) representation with polynomial chaos expansion (PCE), denoted as LR-PCE-ΔV, for a unified multi-contingency voltage assessment. For each model-construction realization, the voltage increment ΔV is calculated as the difference between the post-contingency and pre-contingency bus voltages. The voltage increments under all contingencies are then stacked to form a joint multi-contingency voltage-increment response matrix. Singular value decomposition (SVD) is applied to extract the dominant modes, and one PCE surrogate is constructed for the coefficient of each retained mode. For a new input realization, the predicted modal coefficients are used to reconstruct the voltage increments under all contingencies, after which the corresponding pre-contingency voltages are added to recover the post-contingency voltages. The method is verified on the IEEE 118-bus system with 231 N − 1 contingencies against conventional Monte Carlo simulation (MCS), comprising the pre-contingency AC optimal power flow (ACOPF) and post-contingency AC power flow calculations (ACPF). The proposed method reduces the number of required PCE surrogates from 231 to 21. Compared with the corresponding low-rank model constructed directly from the post-contingency voltages, it reduces the RMSE by 85.9% and achieves a 7.7-fold speedup over conventional MCS while accurately reproducing voltage distributions.