Short-term wind power prediction based on extreme learning machine with error correction

Introduction: Large-scale integration of wind generation brings great challenges to the secure operation of the power systems due to the intermittence nature of wind. The fluctuation of the wind generation has a great impact on the unit commitment. Thus accurate wind power forecasting plays a key role in dealing with the challenges of power system operation under uncertainties in an economical and technical way. Methods: In this paper, a combined approach based on Extreme Learning Machine (ELM) and an error correction model is proposed to predict wind power in the short-term time scale. Firstly an ELM is utilized to forecast the short-term wind power. Then the ultra-short-term wind power forecasting is acquired based on processing the short-term forecasting error by persistence method. Results: For short-term forecasting, the Extreme Learning Machine (ELM) doesn’t perform well. The overall NRMSE (Normalized Root Mean Square Error) of forecasting results for 66 days is 21.09 %. For the ultra-short term forecasting after error correction, most of forecasting errors lie in the interval of [−10 MW, 10 MW]. The error distribution is concentrated and almost unbiased. The overall NRMSE is 5.76 %. Conclusion: The ultra-short-term wind power forecasting accuracy is further improved by using error correction in terms of normalized root mean squared error (NRMSE)

On the indicator of weak sustainability

Facing the difficulties of measuring sustainability, Pearce and Atkinson proposed the Pearce-Atkinson (P-A) indicator, which provides an important direction or method of sustainability measurement. Although the weak sustainability rule is limited in its real world relevance, Pearce and Atkinson's research results show that it can yield some useful insights of sustainability into a system from some aspects. With the improvement of the P-A indicator, it will become more practically useful. At the same time, we should pay more attention to the study of the P-A indicator method with Amsberg's continuum, which will make the indicator for measurement of sustainability change gradually from "weak' to practically "strong'. -from Author

How Green Are the Streets Within the Sixth Ring Road of Beijing? An Analysis Based on Tencent Street View Pictures and the Green View Index

Street greenery, an important urban landscape component, is closely related to people’s physical and mental health. This study employs the green view index (GVI) as a quantitative indicator to evaluate visual greenery from a pedestrian’s perspective and uses an image segmentation method to calculate the quantity of visual greenery from Tencent street view pictures. This article aims to quantify street greenery in the area within the sixth ring road in Beijing, analyse the relations between road parameters and the GVI, and compare the visual greenery of different road types. The authors find that (1) the average GVI value in the study area is low, with low-value clusters inside the third ring road and high-value clusters outside; (2) wider minor roads tend to have higher GVI values than motorways, major roads and provincial roads; and (3) longer roads, except expressways, tend to have higher GVI values. This case study demonstrates that the GVI can effectively represent the quantity of visual greenery along roads. The authors’ methods can be employed to compare street-level visual greenery among different areas or road types and to support urban green space planning and management