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The development in the fields of clean energy, particularly wind and photovoltaic power, generates a large amount of data streams, and how to mine valuable information from these data to improve the efficiency of power generation has become a hot spot of current research. Traditional classification algorithms cannot cope with dynamically changing data streams, so data stream classification techniques are particularly important. The current data stream classification techniques mainly include decision trees, neural networks, Bayesian networks, and other methods, which have been applied to wind power and photovoltaic power data processing in existing research. However, the data drift problem is gradually highlighted due to the dynamic change in data, which significantly impacts the performance of classification algorithms. This paper reviews the latest research on data stream classification technology in wind power and photovoltaic applications. It provides a detailed introduction to the data drift problem in machine learning, which significantly affects algorithm performance. The discussion covers covariate drift, prior probability drift, and concept drift, analyzing their potential impact on the practical deployment of data stream classification methods in wind and photovoltaic power sectors. Finally, by analyzing examples for addressing data drift in energy-system data stream classification, the article highlights the future prospects of data drift research in this field and suggests areas for improvement. Combined with the systematic knowledge of data stream classification techniques and data drift handling presented, it offers valuable insights for future research. [ABSTRACT FROM AUTHOR]

The realization of p-type transparent conductive oxides (TCOs) is a long-standing difficulty because of their generally large hole effective mass and low doping limit ascribed to the strong electronegativity of oxygen. By introducing a Bi atom with a lone-pair s orbital on the B site, double perovskite Ba2BiTaO6 could exhibit a dispersive upper valence band and a small hole effective mass due to the strong sp antibonding coupling. In contrast to n-type TCOs, i.e., ZnO and In2O3, first-principles studies on the intrinsic defects of Ba2BiTaO6 show that it is able to exhibit a good p-type property or stay intrinsic instead of being n-type by tuning the chemical potentials and its dominated defects (VBa, VBi, BaBi, and BiTa) are shallow acceptors, indicating that Ba2BiTaO6 is able to be a promising p-type TCO. This work shows that perovskites could be a class of potential p-type TCOs by introducing strong sp coupling with lone-pair-s-orbital elements. [ABSTRACT FROM AUTHOR]

Ga2O3 is clearly emerging as an important wide band‐gap semiconductor. Band‐gap engineering is now highly demanded for expanding its applications. Alloying with the same group of metal oxides is a straightforward and effective way. In this work, by using hybrid density functional theory calculations, the structural, electronic properties, and phase stability of group IIIA (Al, Ga, In) metal oxides and their ternary alloys (MxGa1‐x)2O3 (M = Al, In) in the corundum and monoclinic phases are systematically investigated. The lattice constants, elastic constants, modulus, formation energies, band‐gaps, band‐gap deformation potentials, band‐edge alignments, band‐gap bowing, and ternary alloy formation energies are obtained. The basic relations between the geometric structure and electronic properties are discussed. It is found that the cation ordered structure is the most stable alloy structure in the monoclinic phase, rather than the random alloy structure as is commonly thought. A phase stability diagram of the (MxGa1‐x)2O3 alloys is established, showing that the stable phase of the alloy changes from the monoclinic phase to the corundum phase when the incorporation of Al2O3 (In2O3) is greater than 69% (76%). These results can be used to understand the relative experimental data and shed some light on the synthesis and device design efforts of Ga2O3. [ABSTRACT FROM AUTHOR]

The atomic structure and electronic properties of aluminum (Al)-related defect complexes in silicon (Si) are investigated using first-principles calculations. Individual substitutional Al (AlSi), interstitial Al (Ali) and their possible complex configurations with oxygen (O) atoms are studied. We find a unique stable complex configuration consisting of an Ali and an oxygen dimer, Ali-2Oi, which introduces deep levels in the band gap of Si. The formation energies of the Ali-2Oi complexes could be lower than that of individual Ali atoms under oxygen-rich conditions. The formation of Ali-2Oi complexes may explain the experimental observation that the coexistence of Al and O results in reduced carrier lifetime in Si wafers. [ABSTRACT FROM AUTHOR]

By using the first-principles calculation methods, the stable structures of oxygen incorporated [formula] surface of AlN and InN are explored in comparison with that of GaN. The one for AlN is found to be VAl-(ON)3, a complex of Al vacancy and three substitutional O in N sites, while the one for InN is consistent with that of GaN, which is comprised by two monolayers of O replacing the N atoms, denoted by 2(ON). The stabilization mechanisms of the two surface structures and the origin of the discrepancy between AlN and GaN are further given by analyzing their electronic structures. [ABSTRACT FROM AUTHOR]

Using first-principles methods, we investigated the atomic relaxations, electronic structure, and formation energies of nonpassivated AlN nanowires along [0001] directions. We find that all the nanowires prefer to have [formula] lateral facets and all the wires with [formula] lateral facets are semiconductors with a direct band gap. However, surface states that arise from the facet atoms exist inside the bulklike band gap, which can have a large effect on the optoelectronic properties of the nanowires. Our calculated formation energies of the nanowires show that there is a sublinear relationship between the formation energy and surface-to-volume ratio, indicating that the surface effect is localized and becomes more important for small nanowires. [ABSTRACT FROM AUTHOR]