Search

In response to the volatility of photovoltaic power generation, this paper proposes a short-term photovoltaic power generation prediction model (HWOA-MVMD-TPA-TCN) based on a Hybrid Whale Optimization Algorithm (HWOA), multivariate variational mode decomposition (MVMD), temporal pattern attention mechanism (TPA), and temporal convolutional network (TCN). In order to improve the accuracy of photovoltaic power generation forecasting, HWOA-MVMD is used for data decomposition, the Minimum Mode Overlap Component (MMOC) is used as the objective function, the photovoltaic power generation sequence is decomposed into finite Intrinsic Mode Functions (IMFs) according to the optimal solution, and the training set is formed with key meteorological variable data such as total radiation (unit: W/m2), ambient temperature, and humidity. Then, the TPA-TCN model is used to train the sub-sequences, the final predicted values are obtained after superimposing the reconstruction of the prediction results, and finally the prediction error of the photovoltaic power generation data is studied. The proposed method is applied to real photovoltaic power generation data from a commercial center in Tianjin and is compared with HWOA-MVMD-BiLSTM, GWO-MVMD-TPA-TCN, and TPA-TCN prediction models. The simulation results demonstrate that the MAE value of the forecast method proposed in this paper is 1.95 MW and the RMSE value is 2.55 MW, which can be reduced by up to 33.74% and 38.85%, respectively. The HWOA-MVMD-TPA-TCN-based short-term photovoltaic power generation prediction model presented in this paper achieves higher prediction accuracy and superior performance, serving as a valuable reference for related research. [ABSTRACT FROM AUTHOR]

In order to improve the forecasting accuracy of short-term power load，a power load forecasting approach based on ensemble empirical mode decomposition(EEMD)-sample entropy(SE) and genetic algorithm(GA) for RBF neural network optimization is proposed in the paper. The EEMD decomposition method is used to decompose the load sequence adaptively, and according to sample entropy to combine the subsequences with similar complexity, which effectively reduces the scale of operation. Based on the difference of sub-sequence complexity, the corresponding RBF neural network model is constructed. The genetic algorithm is used to avoid the neural network falling into the local optimal and convergence problem. Then the new sub-sequences are forecasted and superimposed to obtain the final forecasting result. The simulated results show that the prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting.

Wufeng Formation shale is an important source rock of unconventional hydrocarbons in the Lower Paleozoic shales of Sichuan Basin. However, the study on its provenance and paleoclimate is still relatively limited. In this study, mineralogical and geochemical data of the shales from the Upper Ordovician Wufeng Formation in southwestern China has been used to interpret the provenance and conditions of weathering and paleoclimate. The Wufeng shales have intermediate to high SiO2 (57.72–82.38 wt. %, av. = 68.84 wt. %) and Al2O3 (5.26–16.17 wt. %, av. = 10.62 wt. %), are rich in transition metal elements (i.e. V, Ni, Cu, Co and Cr) and Y as well as moderate depletion in Na2O and Sr, relative to the concentrations of the upper continental crust (UCC). In the chondrite-normalized (CN) rare earth elements (REE) distributions, these rocks display light REE (LREE) enrichment (La/YbCN = 6.69–12.63, av. = 9.28), flat heavy REE (HREE) (Gd/YbCN = 1.35–2.41, av. = 1.70), and clearly negative Eu anomalies (Euan = 0.50–0.66, av. = 0.58), showing similar characteristics with the CN post-Archean Australian Average Shales (PAAS). Wufeng Formation shales are immature composition without evident recycling sediments, and they are originated from an intermediate-felsic igneous source composed of tonalite–trondhjemite–granodiorite (TTG), granitic and andesitic igneous rocks. The chemical weathering conditions of studied shales decreased from moderate to low in the provenance region, suggesting a gradual cooling trend of the climate at Late Ordovician Thus, this article will be helpful to discern the provenance and variations of chemical weathering conditions and paleoclimate of Wufeng Formation shales. [ABSTRACT FROM AUTHOR]