Toward Carbon-Neutral Electric Power Systems in the New York State: a Novel Multi-Scale Bottom-Up Optimization Framework Coupled with Machine Learning for Capacity Planning at Hourly Resolution

2021 ◽  
Author(s):  
Ning Zhao ◽  
Fengqi You
Keyword(s):  
Machine Learning ◽  
New York ◽  
Power Systems ◽  
Electric Power ◽  
Capacity Planning ◽  
New York State ◽  
Electric Power Systems ◽  
York State ◽  
Multi Scale ◽  
Optimization Framework

scholarly journals Estimation of PM2.5 Concentrations in New York State: Understanding the Influence of Vertical Mixing on Surface PM2.5 Using Machine Learning

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1303
Author(s):  
Wei-Ting Hung ◽  
Cheng-Hsuan (Sarah) Lu ◽  
Stefano Alessandrini ◽  
Rajesh Kumar ◽  
Chin-An Lin
Keyword(s):  
Machine Learning ◽  
New York ◽  
New York State ◽  
Vertical Mixing ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Joint Analysis ◽  
Ann Model ◽  
York State ◽  
Ann Models

In New York State (NYS), episodic high fine particulate matter (PM2.5) concentrations associated with aerosols originated from the Midwest, Mid-Atlantic, and Pacific Northwest states have been reported. In this study, machine learning techniques, including multiple linear regression (MLR) and artificial neural network (ANN), were used to estimate surface PM2.5 mass concentrations at air quality monitoring sites in NYS during the summers of 2016–2019. Various predictors were considered, including meteorological, aerosol, and geographic predictors. Vertical predictors, designed as the indicators of vertical mixing and aloft aerosols, were also applied. Overall, the ANN models performed better than the MLR models, and the application of vertical predictors generally improved the accuracy of PM2.5 estimation of the ANN models. The leave-one-out cross-validation results showed significant cross-site variations and were able to present the different predictor-PM2.5 correlations at the sites with different PM2.5 characteristics. In addition, a joint analysis of regression coefficients from the MLR model and variable importance from the ANN model provided insights into the contributions of selected predictors to PM2.5 concentrations. The improvements in model performance due to aloft aerosols were relatively minor, probably due to the limited cases of aloft aerosols in current datasets.

Machine learning driven smart electric power systems: Current trends and new perspectives

2020 ◽  
Vol 272 ◽  
pp. 115237 ◽  
Author(s):  
Muhammad Sohail Ibrahim ◽  
Wei Dong ◽  
Qiang Yang
Keyword(s):  
Machine Learning ◽  
Power Systems ◽  
Electric Power ◽  
Electric Power Systems ◽  
Current Trends

Measurement-driven optimal control of utility-scale power systems: A New York State grid perspective

2020 ◽  
Vol 115 ◽  
pp. 105470
Author(s):  
Sayak Mukherjee ◽  
Saman Babaei ◽  
Aranya Chakrabortty ◽  
Bruce Fardanesh
Keyword(s):  
New York ◽  
Optimal Control ◽  
Power Systems ◽  
New York State ◽  
York State ◽  
Utility Scale
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