Statistical Machine Learning for Power Flow Analysis Considering the Influence of Weather Factors on Photovoltaic Power Generation.

It is generally accepted that the impact of weather variation is gradually increasing in modern distribution networks with the integration of high-proportion photovoltaic (PV) power generation and weather-sensitive loads. This article analyzes power flow using a novel stochastic weather generator (SWG) based on statistical machine learning (SML). The proposed SML model, which incorporates generative adversarial networks (GANs), probability theory, and information theory, enables the generation and evaluation of simulated hourly weather data throughout the year. The GAN model captures various weather variation characteristics, including weather uncertainties, diurnal variations, and seasonal patterns. Compared to shallow learning models, the proposed deep learning model exhibits significant advantages in stochastic weather simulation. The simulated data generated by the proposed model closely resemble real data in terms of time-series regularity, integrity, and stochasticity. The SWG is applied to model PV power generation and weather-sensitive loads. Then, we actively conduct a power flow analysis (PFA) on a real distribution network in Guangdong, China, using simulated data for an entire year. The results provide evidence that the GAN-based SWG surpasses the shallow machine learning approach in terms of accuracy. The proposed model ensures accurate analysis of weather-related power flow and provides valuable insights for the analysis, planning, and design of distribution networks.

Chloroplast Genome Comparison and Phylogenetic Analysis of the Commercial Variety Actinidia chinensis 'Hongyang'.

Actinidia chinensis 'Hongyang', also known as red yangtao (red heart kiwifruit), is a vine fruit tree native to China possessing significant nutritional and economic value. However, information on its genetic diversity and phylogeny is still very limited. The first chloroplast (cp) genome of A. chinensis 'Hongyang' cultivated in China was sequenced using de novo technology in this study. A. chinensis 'Hongyang' possesses a cp genome that spans 156,267 base pairs (bp), exhibiting an overall GC content of 37.20%. There were 132 genes that were annotated, with 85 of them being protein-coding genes, 39 transfer RNA (tRNA) genes, and 8 ribosomal RNA (rRNA) genes. A total of 49 microsatellite sequences (SSRs) were detected, mainly single nucleotide repeats, mostly consisting of A or T base repeats. Compared with 14 other species, the cp genomes of A. chinensis 'Hongyang' were biased towards the use of codons containing A/U, and the non-protein coding regions in the A. chinensis 'Hongyang' cpDNA showed greater variation than the coding regions. The nucleotide polymorphism analysis (Pi) yielded nine highly variable region hotspots, most in the large single copy (LSC) region. The cp genome boundary analysis revealed a conservative order of gene arrangement in the inverted repeats (IRs) region of the cp genomes of 15 Actinidia plants, with small expansions and contractions of the boundaries. Furthermore, phylogenetic tree indicated that A. chinensis 'Hongyang' was the closest relative to A. indochinensis. This research provides a useful basis for future genetic and evolutionary studies of A. chinensis 'Hongyang', and enriches the biological information of Actinidia species.

A Review of Key Technologies and Trends in the Development of Integrated Heating and Power Systems in Agriculture.

Petroleum agriculture, characterized by mechanization and chemistry, is developing rapidly in China. However, petroleum agriculture has not only brought food safety problems, but also caused great obstacles to the sustainable development of society. In view of the disadvantages of oil agriculture, we provide an upgrading plan for energy systems in agriculture. This work can help reduce carbon emissions and improve food security. We introduce the most advanced technologies in Chinese agricultural development and the technical scope includes new agricultural energy power generation, agricultural energy use and the safe operation of agricultural energy systems. We describe the detailed data of agricultural bioenvironmental and energy engineering to clarify the level of agricultural energy efficiency in China. The overall conclusion of this paper is that the deep integration of agriculture and energy internet has become the development trend of agricultural energy systems.