A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid

A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU

Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Multiverse optimization algorithm (MVO), and the Gated Recurrent Unit (GRU) based on Rectified Adam (RAdam) optimizer. Firstly, the model uses the CEEMDAN algorithm to decompose the original electric load data into subsequences of different frequencies, and the dominant factors are extracted from the subsequences. Then, a GRU network based on the RAdam optimizer was built to perform the forecasting of the subsequences using the existing subsequences data and the associated influencing factors as the data set. Meanwhile, the parameters of the GRU network were optimized with the MVO optimization algorithm for the prediction problems of different subsequences. Finally, the prediction results of each subsequence were superimposed to obtain the final prediction results. The proposed combined prediction method was implemented in a case study of a substation in Weinan, China, and the prediction accuracy was compared with the traditional prediction method. The prediction accuracy index shows that the Root Mean Square Error of the prediction results of the proposed model is 80.18% lower than that of the traditional method, and the prediction accuracy error is controlled within 2%, indicating that the proposed model is better than the traditional method. This will have a favorable impact on the safe and stable operation of the power grid

Effect of Ag Doping on Photobleaching in Ge28Sb12Se60 Chalcogenide Films

Chalcogenide glass is an optical material with excellent mid-infrared and far-infrared penetration properties. The silver-doped Ge28Sb12Se60 (GSS) chalcogenide films in this paper were deposited on a glass substrate by the co-evaporation technique. A continuous laser with different power outputs was then used to scan the glass material at a constant speed, and the photobleaching (PB) effects were observed using optical microscopy. The results show that silver doping can speed up the PB of GSS film only under high-power laser irradiation. While silver doping helps to speed up the PB effect, it also increases the risk of film damage. This study is beneficial in the development of embedded optical waveguide structures

Optimization of Water and Energy Spatial Patterns in the Cascade Pump Station Irrigation District

Cascade pump station irrigation districts (CPSIDs) consume large quantities of water and energy. Water- and energy-saving results and income increases are guaranteed under the sustainable development of the CPSID. The CPSID is divided into several sub-districts based on the elevation difference of topography and pump station distributions. The spatial patterns of crops and irrigation technologies can be changed by adjusting crop planting structures and developing drip irrigation in each sub-district. Its optimization will change the spatial patterns of irrigation water and energy consumption to achieve water- and energy-saving results, increase income, and provide an ecological advantage. To obtain the optimal spatial patterns of water and energy in the CPSID, a multi-objective linear programming model of minimum irrigation water consumption, minimum energy consumption, and highest crop output value was established. This model was applied to the Jingdian Phase I Irrigation District in northwest China, and an optimal scheme of water and energy spatial patterns was obtained. Compared with the present situation, the optimal scheme could save water by 26.18%, save energy by 29.38%, and increase income by 29.55%. The increased investment in the drip irrigation project would lead to reduced irrigation water and energy consumption and increased crop output value. The research results provide a scientific basis for the sustainable development of agriculture and ecological environment protection in the CPSID

Simulation of Water–Energy Nexus of the Spatial Patterns of Crops and Irrigation Technologies in the Cascade Pump Station Irrigation District

Cascade pump station irrigation districts (CPSIDs) consume vast amounts of irrigation water and energy. The aim of this study was to adjust the spatial patterns of crops and irrigation technologies in the CPSID to reduce the consumption of water and energy under the condition of conserving crop irrigation water. The irrigation district (ID) is divided into several sub-districts according to the topography elevation difference and the distribution of cascade pump stations (CPSs). The mathematical models of the irrigation water and energy consumption in each sub-district were established based on the relationship between the spatial patterns of crops and irrigation technologies in each sub-district. According to the present situation of the Jingdian Phase I Irrigation District in the arid region of northwest China, three modes of adjusting the crop planting structure and drip irrigation area were proposed. Based on the combination of these modes, three schemes of the spatial patterns of crops and irrigation technologies were generated. The annual energy consumption and irrigation water consumption of each sub-district in the ID of these three schemes were obtained through simulation. Compared with the present spatial patterns of crops and irrigation technologies in the Jingdian Phase I Irrigation District, Scheme 3 has the best water-saving and energy-saving effects, with an annual water saving and energy saving of 1753 × 104 m3 and 2898 × 104 kWh, and the water-saving rate and energy-saving rate were 12.34% and 15.74%, respectively. This paper also shows that the synchronous adjustment of crops and irrigation technologies among the sub-districts of ID can achieve significant water-saving and energy-saving effects

Quantifying the Water-Energy-Food Nexus: Current Status and Trends

Water, energy, and food are lifelines for modern societies. The continuously rising world population, growing desires for higher living standards, and inextricable links among the three sectors make the water-energy-food (WEF) nexus a vibrant research pursuit. For the integrated delivery of WEF systems, quantifying WEF connections helps understand synergies and trade-offs across the water, energy, and food sectors, and thus is a critical initial step toward integrated WEF nexus modeling and management. However, current WEF interconnection quantifications encounter methodological hurdles. Also, existing calculation results are scattered across a wide collection of studies in multiple disciplines, which increases data collection and interpretation difficulties. To advance robust WEF nexus quantifications and further contribute to integrated WEF systems modeling and management, this study: (i) summarizes the estimate results to date on WEF interconnections; (ii) analyzes methodological and practical challenges associated with WEF interconnection calculations; and (iii) points out opportunities for enabling robust WEF nexus quantifications in the future

Mechanical and Dielectric Strength of Laminated Epoxy Dielectric Graded Materials

Laminated epoxy dielectric graded material is a commonly used insulating material with broad application prospects in power equipment. The interlaminar interfaces of laminated epoxy dielectric material between different layers form during its lamination process, and these interfaces are the crucial characteristic structures determining the mechanical and dielectric properties of laminated materials. Therefore, in order to gain a thorough understanding of physic properties behind a certain structural motif, it is necessary to study how these interfacial structures influence the mechanical and dielectric performances of graded materials. In this study, double-layered epoxy resin samples with an interlaminar interface are prepared to study their mechanical and dielectric strength. More importantly, the formation mechanism of the interface, as well as its influence on the mechanical and dielectric strength of this laminated material, is discussed. We found that a cross-linking reaction may take place between epoxy resins at the interlaminar interface, and the degree of cross-linking at the interface should be less than that in the bulk. The mechanical strength of the interlaminar interface is weaker than that of the bulk, and it is reduced by less than 40%. Moreover, the interlaminar interface is inclined to trap carriers, which improves the breakdown strength and arc ablation resistance of the laminated material. Our study of interlaminar interface properties could help in designing epoxy dielectric graded materials with better mechanical and dielectric properties

Codon usage pattern of the ancestor of green plants revealed through Rhodophyta

Abstract Rhodophyta are among the closest known relatives of green plants. Studying the codons of their genomes can help us understand the codon usage pattern and characteristics of the ancestor of green plants. By studying the codon usage pattern of all available red algae, it was found that although there are some differences among species, high-bias genes in most red algae prefer codons ending with GC. Correlation analysis, Nc-GC3s plots, parity rule 2 plots, neutrality plot analysis, differential protein region analysis and comparison of the nucleotide content of introns and flanking sequences showed that the bias phenomenon is likely to be influenced by local mutation pressure and natural selection, the latter of which is the dominant factor in terms of translation accuracy and efficiency. It is worth noting that selection on translation accuracy could even be detected in the low-bias genes of individual species. In addition, we identified 15 common optimal codons in seven red algae except for G. sulphuraria for the first time, most of which were found to be complementary and bound to the tRNA genes with the highest copy number. Interestingly, tRNA modification was found for the highly degenerate amino acids of all multicellular red algae and individual unicellular red algae, which indicates that highly biased genes tend to use modified tRNA in translation. Our research not only lays a foundation for exploring the characteristics of codon usage of the red algae as green plant ancestors, but will also facilitate the design and performance of transgenic work in some economic red algae in the future

Proteomic and metabolomic analyses provide insight into the off-flavour of fruits from citrus trees infected with ‘Candidatus Liberibacter asiaticus’

Pathogenic profiling: why fruit from bacteria-infected trees tastes bad The bacteria Candidatus Liberibacter asiaticus (CaLas) causes orange trees to produce poor-tasting fruit thanks to the decreased production of flavour-enhancing proteins, sugars, and metabolites. The University of Florida’s Frederick Gmitter and his team of US and Chinese scientists profiled the proteins and metabolites of healthy Valencia sweet orange trees infected with CaLas, a bacterial pathogen that causes the citrus disease Huanglongbing and reduces the quantity and quality of fruit and juice. The researchers found 123 differentially-expressed proteins and decreased numbers of taste-enhancing constituents, including those mediated by key energy-producing processes. This degradation involved a class of chemicals called terpenoids, which the authors link to poor quality fruit. These results provide insights into the pathogenesis of CaLas infection and could empower future studies to prevent the impact of the bacteria and Huanglongbing infection