Power prediction is now a crucial part of contemporary energy management systems, which is important for the organization and administration of renewable resources. Solar and wind powers are highly dependent upon environmental factors, such as wind speed, temperature, and humidity, making the forecasting problem extremely difficult. The suggested composite model incorporates Long Short-Term Memory (LSTM) and Swarm Intelligence (SI) optimization algorithms to produce a framework that can precisely estimate offshore wind output in the short term, addressing the discrepancies and limits of conventional estimation methods. The Coati optimization algorithm enhances the hyper parameters CNN-LSTM. Optimum hyper parameters improvise learning rate and performance. The day-ahead and hour-ahead short-term predictions RMSE can be decreased by 0.5% and 5.8%, respectively. Compared to GWO-CNN-LSTM, LSTM, CNN, and PSO-CNN-LSTM models, the proposed technique achieves an nMAE of 4.6%, RE 27% and nRMSE of 6.2%. COA-CNN-LSTM outperforms existing techniques in terms of the Granger causality test and Nash-Sutcliffe metric analysis for time series forecasting performance, scores are 0.0992 and 0.98, respectively. Experimental results show precise and definitive wind power-making predictions for the management of renewable energy conversion networks. The presented model contributes positively to the body of knowledge and development of clean energy. 

COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications

Real-time locating systems applications in construction

Automatic building detection in orthophotos via a machine learning approach.Flexible framework that exploits supervised learning.Applying the covariance descriptor to the building detection problem.An extended performance study of several combination segmentation-descriptor.Classification performance is obtained with K-NN, Partial Least Square and SVM. Building detection from aerial images has many applications in fields like urban planning, real-estate management, and disaster relief. In the last two decades, a large variety of methods on automatic building detection have been proposed in the remote sensing literature. Many of these approaches make use of local features to classify each pixel or segment to an object label, therefore involving an extra step to fuse pixelwise decisions. This paper presents a generic framework that exploits recent advances in image segmentation and region descriptors extraction for the automatic and accurate detection of buildings on aerial orthophotos. The proposed solution is supervised in the sense that appearances of buildings are learnt from examples. For the first time in the context of building detection, we use the matrix covariance descriptor, which proves to be very informative and compact. Moreover, we introduce a principled evaluation that allows selecting the best pair segmentation algorithm-region descriptor for the task of building detection. Finally, we provide a performance evaluation at pixel level using different classifiers. This evaluation is conducted over 200 buildings using different segmentation algorithms and descriptors. The performance analysis quantifies the quality of both the image segmentation and the descriptor used. The proposed approach presents several advantages in terms of scalability, suitability and simplicity with respect to the existing methods. Furthermore, the proposed scheme (detection chain and evaluation) can be deployed for detecting multiple object categories that are present in images and can be used by intelligent systems requiring scene perception and parsing such as intelligent unmanned aerial vehicle navigation and automatic 3D city modeling.

Building detection from orthophotos using a machine learning approach

Improving satellite aerosol optical Depth-PM2.5 correlations using land use regression with microscale geographic predictors in a high-density urban context

Mapping the environment.

Shenyang City is one of the largest and important industrial cities in the northeast in China which plays an important role in the development of regional economy. With the increasing level of urbanization and industrialization, the air pollution problem has also been frequently appeared in the public sight. In order to better understand the distribution and time-varying of the pollutants in this area. A coupled model of WRF/CALPUFF was chosen to simulate the changing trend of sulfur oxides from 30 January 00 h UTC to 1 January 00 h UTC(winter episode) in 2014. By comparing the two datasets of measured and simulated, a conclusion that the effects of terrain characteristic and wind speed and direction on SO2 are better reproduced by the model than the effects of temperature. Also, the result shows that the WRF/CALPUFF model can reproduce satisfactorily meteorology fields and SO2 changing trend.

Research on Regional Environmental Pollution Analysis Technology Based on Atmospheric Numerical Model in Shenyang City


 In the present research, a statistical analysis of all pollution incidents occurring from 2015 to 2019 in the cities of the urban agglomeration centered on Shenyang was performed. The results indicated that heavy pollution mainly occurred during the heating season, and the main pollutants were (particulate matter) PM2.5 and PM10. It was also determined that the heavy pollution that occurred during the heating season in Shenyang was of the soot type. The weather research forecast-chemistry (WRF-Chem) was used to simulate the meteorological elements and particle concentration during the two heavy pollution periods in 2019 and compared the simulation data with the monitoring data to verify the simulation performance of the model. Results demonstrated that the model had a better simulation effect on temperature and pressure than on wind speed and wind direction. By comparing the hourly particle concentration data, it was found that the simulation results for pollutants obtained with the WRF-Chem model were lower than the measured values. The simulation effect on PM2.5 was better than that on PM10, and the simulation results were basically consistent in the high- and low-value areas, and the time of peak and valley was basically synchronous. It was proven that the selected parameterization scheme properly simulated the weather situation and changes in pollutants during heavy pollution events in the Shenyang area. These results verified the application value of the WRF-Chem model during the investigation of heavy pollution events.

Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants

Insufficient management to underground utilities often appears as frequent and disorganized street works, unduly prolonged excavation period and underground properties damage. A centralized utility information system can facilitate utility information to share, update and exchange and has irreplaceable advantages in managing underground space. In order to fully utilize a utility information system, the fundamental component –utility as-built records must be complete, accurate and reliable. However, currently, the data quality of as-built records in many cities remain unsatisfactory, which also obstructs the process of building “Smart City”. The purpose of this paper is to investigate governance issues with as-built records in worldwide cities, in order to provide insight to current manage mechanism. The findings indicate that proper governance and management should be carried out to improve the data quality of as-built records in the long run.

Geo-Informatics in Resource Management and Sustainable Ecosystem

A study designed different parameter schemes for a weather research and forecasting model to simulate hourly winter and summer wind speed and direction at a wind-farm tower in China. The schemes were found to reproduce the local wind conditions accurately and simulate wind direction very well. The results showed that one scheme performed better for the wind speed and wind direction simulation at 10 m and 90 m heights in the winter, while another performed better for simulation at 10 m height in the summer. The results for these schemes showed that the correlation coefficient of wind speed and wind direction was 0.75 and 0.62 in winter and 0.55 and 0.44 in summer, respectively. Overall the parameterised model exhibited better simulation of the wind speed than wind direction, and was better in winter than in summer.

Wind simulation using different parameters in a weather research and forecasting model

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Research on Regional Environmental Pollution Analysis Technology Based on Atmospheric Numerical Model in Shenyang City

Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants

Geo-Informatics in Resource Management and Sustainable Ecosystem

Wind simulation using different parameters in a weather research and forecasting model