Continuous and Distribution-free Probabilistic Wind Power Forecasting: A Conditional Normalizing Flow Approach

We present a data-driven approach for probabilistic wind power forecasting based on conditional normalizing flow (CNF). In contrast with the existing, this approach is distribution-free (as for non-parametric and quantile-based approaches) and can directly yield continuous probability densities, hence avoiding quantile crossing. It relies on a base distribution and a set of bijective mappings. Both the shape parameters of the base distribution and the bijective mappings are approximated with neural networks. Spline-based conditional normalizing flow is considered owing to its non-affine characteristics. Over the training phase, the model sequentially maps input examples onto samples of base distribution, given the conditional contexts, where parameters are estimated through maximum likelihood. To issue probabilistic forecasts, one eventually maps samples of the base distribution into samples of a desired distribution. Case studies based on open datasets validate the effectiveness of the proposed model, and allows us to discuss its advantages and caveats with respect to the state of the art.Comment: The second revision to IEEE Transactions on Sustainable Energ

Association of Aortic Stiffness and Cognitive Decline: A Systematic Review and Meta-Analysis

Background: Increased aortic stiffness has been found to be associated with cognitive function decline, but the evidence is still under debate. It is of great significance to elucidate the evidence in this debate to help make primary prevention decisions to slow cognitive decline in our routine clinical practice.Methods: Electronic databases of PubMed, EMBASE, and Cochrane Library were systematically searched to identify peer-reviewed articles published in English from January 1, 1986, to March 16, 2020, that reported the association between aortic stiffness and cognitive function. Studies that reported the association between aortic pulse wave velocity (PWV) and cognitive function, cognitive impairment, and dementia were included in the analysis.Results: Thirty-nine studies were included in the qualitative analysis, and 29 studies were included in the quantitative analysis. The aortic PWV was inversely associated with memory and processing speed in the cross-sectional analysis. In the longitudinal analysis, the high category of aortic PWV was 44% increased risk of cognitive impairment (OR 1.44; 95% CI 1.24–1.85) compared with low PWV, and the risk of cognitive impairment increased 3.9% (OR 1.039; 95% CI 1.005–1.073) per 1 m/s increase in aortic PWV. Besides, meta-regression analysis showed that age significantly increased the association between high aortic PWV and cognitive impairment risk.Conclusion: Aortic stiffness measured by aortic PWV was inversely associated with memory and processing speed and could be an independent predictor for cognitive impairment, especially for older individuals

Examining Chinese Corporate's Philanthropy for the Sustainable Development Goals

Enterprises, as principal decision-making units in the market and cells of the national economy, can play a crucial role in promoting sustainable economic, social, and ecological development. In response to the call of the United Nations, enterprises in China have taken on more responsibility in the joint efforts toward Sustainable Development Goals. In 2019, the total amount of social donations in China reached totaling 170 billion yuan, and 55% of them are from enterprises (China Charity Alliance, 2020). However, research tracking and assessing their impacts and outcomes achieved is scarce. In order to investigate the patterns, mechanisms, and outcomes of corporate philanthropy in China under the UN 2030 Agenda, this study intends to examine Chinese enter prises' philanthropy for SDGs through both quantitative and qualitative analysis. We 1) construct a donation database towards SDGs covering over 6 million donation events from 2008 to 2020 with outer partners, 2) conduct four quantitative analyses of corporate donations to SDGs in four different scenarios including contributions of leading companies, the contributions in 2015, the donating preferences of companies in the power industry, and the enterprises' donating behaviors during the Covid 19 pandemic, and 3) provide several case studies on Chinese enterprises' donations to SDGs to explore who the stakeholders engaged in the process are and how the donations could be managed and finally go towards certain sustainable development goals. Accordingly, a panorama of Chinese enterprises' philanthropy for SDGs is developed eventually and suggestions are proposed for relevant stakeholders to facilitate the corporate participation in SDGs through philanthropy.</p

Elastic energy distribution of local area packetized power networks to mitigate distribution level load fluctuation

Featured by distributed energy storing and time division multiplexing transmission, dc packetized-power distribution has great potential in compensating inelastic load fluctuations at the demand side. In this paper, we investigate an ac distribution grid integrated with local area packetized-power networks (LAPPNs). LAPPNs, as the dc sector, enable a store-then-consume mechanism, whereby the sectoral loads characterized as elastic ones, can be manipulated to reduce the distribution level load fluctuation. A multi-mode energy distribution is proposed for the dc sector, including an advanced energy distribution as the `storing' phase utilizing conventionally non-peak hours, and an open energy distribution as the `consumption' phase to further compensate the ac sector load fluctuation on real-time basis. We develop a cross-time hierarchical energy distribution scheme to coordinate energy distribution of dc sector and ac sector and exploit the elasticity of dc sector loads on both day-ahead basis and real-time basis, jointly resulting in a significant mitigation of distribution load fluctuation. Power distribution optimizations are separately formulated and solved for different procedures of the scheme. Simulations have been conducted to demonstrate the effectiveness of the scheme in reducing distribution level load fluctuation and to reveal facts on how much dc elastic loads is needed for fluctuation reduction in a scenario in North America.Featured by distributed energy storing and time division multiplexing transmission, dc packetized-power distribution has great potential in compensating inelastic load fluctuations at the demand side. In this paper, we investigate an ac distribution grid integrated with local area packetized-power networks (LAPPNs). LAPPNs, as the dc sector, enable a store-then-consume mechanism, whereby the sectoral loads characterized as elastic ones, can be manipulated to reduce the distribution level load fluctuation. A multi-mode energy distribution is proposed for the dc sector, including an advanced energy distribution as the `storing' phase utilizing conventionally non-peak hours, and an open energy distribution as the `consumption' phase to further compensate the ac sector load fluctuation on real-time basis. We develop a cross-time hierarchical energy distribution scheme to coordinate energy distribution of dc sector and ac sector and exploit the elasticity of dc sector loads on both day-ahead basis and real-time basis, jointly resulting in a significant mitigation of distribution load fluctuation. Power distribution optimizations are separately formulated and solved for different procedures of the scheme. Simulations have been conducted to demonstrate the effectiveness of the scheme in reducing distribution level load fluctuation and to reveal facts on how much dc elastic loads is needed for fluctuation reduction in a scenario in North America

Effects of sample test conditions on analysis results of coal qualities based on near infra-red method

To solve the problem of signal-noise ratio change caused by spectroscopy absorbance, scattering and noise interference resulting from sample accumulation, which caused analysis error, effects of sample state and test condition on near infra-red analysis results were studied. near infra-red spectrograms were collected under different thickness, loading times and different loading tightness, and the data were compressed using principal component analysis. BP neural network models were established based on genetic algorithm, and the prediction performance of different sample state models were compared by determination coefficient, and the sample test conditions were optimized. The experimental results show that repeated loading times and sample tightness will not significantly improve predictive capability of the model. While the sample loading thickness is 0.5 mm, the determination coefficient of testing set R2 of moisture, ash, volatile matter and heat prediction model respectively are 0.936 6, 0.791 6, 0.894 9 and 0.857 5, which show good performace of the model