物理教学中的“预伏”

为了照顾后面的教学，提前“预伏”，对沟通知识之间的联系、降低教学难度，能起一定的作用。本文试对各种预伏的特点谈谈自己的一些粗浅看法。

Short-term prediction of photovoltaic power generation based on LMD-EE-ESN with error correction

Considering the instability of the output power of photovoltaic(PV)generation system,to improve the power regulation ability of PV power during grid-connected operation,based on the quantitative analysis of meteorological conditions,a short-term prediction method of PV power based on LMD-EE-ESN with iterative error correction was proposed.Firstly,through the fuzzy clustering processing of meteorological conditions,taking the power curves of PV power generation in sunny,rainy or snowy,cloudy,and changeable weather as the reference,the local mean decomposition(LMD)was carried out respectively,and their energy entropy(EE)was taken as the meteorological characteristics.Then,the historical generation power series was decomposed by LMD algorithm,and the hierarchical prediction of the power curve was realized by echo state network(ESN)prediction algorithm combined with meteorological characteristics.Finally,the iterative error theory was applied to the correction of power prediction results.The analysis of the historical data in the PV power generation system shows that this method avoids the influence of meteorological conditions in the short-term prediction of PV output power,and improves the accuracy of power prediction on the condition of hierarchical prediction and iterative error correction.

Quantitative analysis of geological ore-controlling factors and stereoscopic quantitative prediction of concealed ore bodies

To address the issues for assessing and prospecting the replaceable resource of crisis mines, a geological ore-controlling field model and a mineralization distribution field model were proposed from the viewpoint of field analysis. By dint of solving the field models through transferring the continuous models into the discrete ones, the relationship between the geological ore-controlling effect field and the mineralization distribution field was analyzed, and the quantitative and located parameters were extracted for describing the geological factors controlling mineralization enrichment. The method was applied to the 3-dimensional localization and quantitative prediction for concealed ore bodies in the depths and margins of the Daehang mine in Guangxi, China, and the 3-dimensional distribution models of mineralization indexes and ore-controlling factors such as magmatic rocks, strata, faults, lithology and folds were built. With the methods of statistical analysis and the non-linear programming, the quantitative index set of the geological ore-controlling factors was obtained. In addition, the stereoscopic located and quantitative prediction models were set up by exploring the relationship between the mineralization indexes and the geological ore-controlling factors. So far, some concealed ore bodies with the resource volume of a medium-sized mineral deposit are found in the deep parts of the Dachang Mine by means of the deep prospecting drills following the prediction results, from which the effectiveness of the predication models and results is proved.

Impacts of different radiation schemes on the prediction of solar radiation and photovoltaic power

The output power of a photovoltaic system largely depends on the amount of solar radiation that can be received by the photovoltaic panel, and the solar radiation energy reaching the ground is affected by the radiation transmission process. However, in engineering practice, numerical simulation prediction schemes tend to adopt a kind of radiation scheme, and the prediction of solar radiation and photovoltaic power cannot always meet the prediction accuracy. In this paper, NCEP-NCAR reanalysis data are used as the initial field, and a variety of radiation parameterization schemes are used to produce simulations for the Xinjiang area. Through analysis of examples, it is found that the simulation results differ greatly depending on the radiation parameterization scheme employed, with the maximum absolute error of the total radiation and the predicted power being 106.67 W m-2 and 3.5 MW, respectively. Meanwhile, the mean absolute percentage error of the total radiation ranges from 8.6% to 17.3%, and that of the predicted power from 11.3% to 20.2%. Having analyzed the simulation results of the different radiation parameterization schemes, we conclude that the RRTM/Dudhia and CAM （Community Atmospheric Model） schemes are the most appropriate when under clear-weather conditions.

PV Power Short-Term Forecasting Model Based on the Data Gathered from Monitoring Network

The degree of accuracy in predicting the photovoltaic power generation plays an important role in appropriate allocations and economic operations of the power plants based on the generating capacity data gathered from the geographically separated photovoltaic plants through network. In this paper, a forecasting model is designed with an optimization algorithm which is developed with the combination of PSO （Particle Swarm Optimization） and BP （Back Propagation） neural network. The proposed model is further validated and the experiment results show that the predication model assures the prediction accuracy regardless the day type transitions and other relevant factors, in the proposed model, the prediction error rate is worth less than 20% in all different climatic conditions and most of the prediction error accuracy is less than 10% in sunny day, and whose precision satisfies the management requirements of the power grid companies, reflecting the significance of the proposed model in engineering applications.

Approaches to location prognosis of concealed ore deposits (bodies) of productive mines

This paper demonstrates the channels and methods for location prognosis of concealed ore deposits (bodies) in the deep seated and surrounding districts of productive mines in accordance with their special features. The system frame map is built, from quick exploration in the field to the rapid building of a model indoors. The main research points of location prognosis are also discussed in the paper, which include: 1) integrating the location with the surrounding geological areas, microscopic with macroscopic; 2) analyzing and synthesizing all geological information of different levels, depths and aspects; 3) laying stress on mineralization series; 4) paying attention to the study of the distribution law of ore bodies; 5) introducing the theory of nonlinear dynamics of ore forming processes to ordinary static prognosis; 6) the necessity of the geophysical me thod in recovering information of concealed ore bodies; 7) the combination of all kinds of geology, geophysics, geochemistry and remote sensing methods.

Short-term photovoltaic power prediction using combined K-SVD-OMP and KELM method

For photovoltaic power prediction,a kind of sparse representation modeling method using feature extraction techniques is proposed.Firstly,all these factors affecting the photovoltaic power output are regarded as the input data of the model.Next,the dictionary learning techniques using the K-mean singular value decomposition(K-SVD)algorithm and the orthogonal matching pursuit(OMP)algorithm are used to obtain the corresponding sparse encoding based on all the input data,i.e.the initial dictionary.Then,to build the global prediction model,the sparse coding vectors are used as the input of the model of the kernel extreme learning machine(KELM).Finally,to verify the effectiveness of the combined K-SVD-OMP and KELM method,the proposed method is applied to a instance of the photovoltaic power prediction.Compared with KELM,SVM and ELM under the same conditions,experimental results show that different combined sparse representation methods achieve better prediction results,among which the combined K-SVD-OMP and KELM method shows better prediction results and modeling accuracy.

Machine learning strategies for lithostratigraphic classification based on geochemical sampling data: A case study in area of Chahanwusu River, Qinghai Province, China

Based on the complex correlation between the geochemical element distribution patterns at the surface and the types of bedrock and the powerful capabilities in capturing subtle of machine learning algorithms,four machine learning algorithms,namely,decision tree(DT),random forest(RF),XGBoost(XGB),and LightGBM(LGBM),were implemented for the lithostratigraphic classification and lithostratigraphic prediction of a quaternary coverage area based on stream sediment geochemical sampling data in the Chahanwusu River of Dulan County,Qinghai Province,China.The local Moran’s I to represent the features of spatial autocorrelations,and terrain factors to represent the features of surface geological processes,were calculated as additional features.The accuracy,precision,recall,and F1 scores were chosen as the evaluation indices and Voronoi diagrams were applied for visualization.The results indicate that XGB and LGBM models both performed well.They not only obtained relatively satisfactory classification performance but also predicted lithostratigraphic types of the Quaternary coverage area that are essentially consistent with their neighborhoods which have the known types.It is feasible to classify the lithostratigraphic types through the concentrations of geochemical elements in the sediments,and the XGB and LGBM algorithms are recommended for lithostratigraphic classification.

Statistical Modeling of Energy Production by Photovoltaic Farms

This paper introduces several alternative statistical approaches to modeling and prediction of electric energy generated by photovoltaic farms. The statistical models use outputs of a numerical weather prediction model as their inputs. Presented statistical models allow for easy-to-compute predictions, both in temporal sense and for out-of-sample individual farms. Model performance is illustrated on a sample of real photovoltaic farms located in the Czech Republic.

Anodic passivation of Pb-Ag-Nd anode in fluoride-containing H_2SO_4 solution

An attempt was made to build up a thick and compact oxide layer rapidly by pre-treating the Pb-Ag-Nd anode in fluoride-containing H2SO4 solution. The passivation reaction of Pb-Ag-Nd anode during pre-treatment process was investigated using cyclic voltammetry, linear scanning voltammetry, environmental scanning electron microscopy and X-ray diffraction analysis. The results show that Pb F2 and PbSO4 are formed near the potential of Pb/PbSO4 couple. The pre-treatment in fluoride-containing H2SO4 solution contributes to the formation of a thick, compact and adherent passive film. Furthermore, pre-treatment in fluoride-containing H2SO4 solution also facilitates the formation of PbO2 on the anodic layer, and the reason could be attributed to the formation of more PbF2 and PbSO4 during the pre-treatment which tend to transform to PbO2 during the following electrowinning process. In addition, the anodic layer on anode with pre-treatment in fluoride-containing H2SO4 solution is thick and compact, and its predominant composition is β-PbO2. In summary, the pre-treatment in fluoride-containing H2SO4 solution benefits the formation of a desirable protective layer in a short time.