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Green and low-carbon is an essential direction for environmentally sustainable development. Under the vision of carbon neutrality, the entire society has accelerated the development of electrification, resulting in a continuous increase in electricity demand. Therefore, it is of great practical significance to analyze and forecast electricity demand reasonably and accurately. However, existing single forecasting models based on deterministic forecasting are difficult to capture the seasonal characteristics and nonlinear fluctuations in power demand data and obtain high forecasting accuracy. To fill this disadvantage, a hybrid electricity demand forecasting system based on a seasonal selection method, completely non-recursive decomposition strategy, and the sunflower optimization algorithm is proposed. Actual electricity demand data obtained in different seasons in various Australian states were used for the simulation. Empirical results indicate that the seasonality of irregular data is successfully classified by the seasonal selection method; the high-frequency noise of the original data are removed by the completely non-recursive decomposition strategy, and the forecasting accuracy and reliability of the optimized forecasting module are effectively improved. Based on the sunflower optimization algorithm, the kernel parameters and forecasting system are optimized to provide technical support for the development of electricity demand response and environmental energy protection. [ABSTRACT FROM AUTHOR]

Long-term soil chronosequences are valuable model systems for investigating pedogenesis and investigating the process of element coupling. Here, we assessed the coupling relationships among C, Fe, and Fe-reducing bacteria (Anaeromyxobacter, Geobacter, and Shewanella) in a paddy soil chronosequence of approximately 50 to 1000 years. Soils of the chronosequence originated from tidal marsh under nearly identical landscape and climate conditions. During 1000 years of rice cultivation, soil organic carbon (SOC) contents in surface horizons (0–20 cm) increased from 10.4 to 21.8 g kg−1. In contrast, total Fe contents declined from 59.6 to 45.1 g kg−1 during the initial 50 years of paddy rice cultivation and then further decreased at a low rate of 0.004 g kg−1 soil year−1 (equivalent to 10 kg ha−1 soil year−1). Organically complexed Fe oxides (Fep) increased from 219 to 642 mg g−1 with increasing time of pedogenesis, but free total Fe oxides (Fed) and amorphous Fe oxides (Feo) declined at early stage of soil development, followed by a slow accumulation at later stages of the chronosequence. Gene copy numbers of Anaeromyxobacter and Geobacter increased from 4.6 × 105 and 3.6 × 106 copies g−1 to 3.8 × 107 and 3.6 × 107 copies g−1 dry soil with continuous paddy rice cultivation, while concurrently Shewanella gene abundance decreased gradually from 4.5 × 105 to 9.3 × 104 copies g−1 dry soil. Using structural equation modeling (SEM), different coupling relationships were observed among C, Fe, and Fe-reducing bacteria for the first 300 years of paddy chronosequence and thereafter. Overall, all Fe-reducing bacteria did not show consistent variation. With the stable microbial community and iron oxide fractions, the microbially mediated dissimilatory coupling relationship between C and Fe becomes simple during 1000 years of paddy soil development. [ABSTRACT FROM AUTHOR]