Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning

2020 ◽  
pp. 271-283
Author(s):  
Yang Lin ◽  
Irena Koprinska ◽  
Mashud Rana ◽  
Alicia Troncoso
Keyword(s):  
Solar Power ◽  
Sequence Similarity ◽  
Pattern Sequence ◽  
Meta Learning ◽  
Solar Power Forecasting ◽  
Power Forecasting

MPF-Net: A computational multi-regional solar power forecasting framework

2021 ◽  
Vol 151 ◽  
pp. 111559
Author(s):  
Faiza Mehmood ◽  
Muhammad Usman Ghani ◽  
Muhammad Nabeel Asim ◽  
Rehab Shahzadi ◽  
Aamir Mehmood ◽  
...  
Keyword(s):  
Solar Power ◽  
Solar Power Forecasting ◽  
Power Forecasting

scholarly journals Building the Sun4Cast System: Improvements in Solar Power Forecasting

2018 ◽  
Vol 99 (1) ◽  
pp. 121-136 ◽  
Author(s):  
Sue Ellen Haupt ◽  
Branko Kosović ◽  
Tara Jensen ◽  
Jeffrey K. Lazo ◽  
Jared A. Lee ◽  
...  
Keyword(s):  
Decision Making ◽  
Value Chain ◽  
Solar Irradiance ◽  
Collaborative Design ◽  
Solar Power ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Solar Array ◽  
Solar Power Forecasting ◽  
Power Forecasting

Abstract As integration of solar power into the national electric grid rapidly increases, it becomes imperative to improve forecasting of this highly variable renewable resource. Thus, a team of researchers from the public, private, and academic sectors partnered to develop and assess a new solar power forecasting system, Sun4Cast. The partnership focused on improving decision-making for utilities and independent system operators, ultimately resulting in improved grid stability and cost savings for consumers. The project followed a value chain approach to determine key research and technology needs to reach desired results. Sun4Cast integrates various forecasting technologies across a spectrum of temporal and spatial scales to predict surface solar irradiance. Anchoring the system is WRF-Solar, a version of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model optimized for solar irradiance prediction. Forecasts from multiple NWP models are blended via the Dynamic Integrated Forecast (DICast) System, which forms the basis of the system beyond about 6 h. For short-range (0–6 h) forecasts, Sun4Cast leverages several observation-based nowcasting technologies. These technologies are blended via the Nowcasting Expert System Integrator (NESI). The NESI and DICast systems are subsequently blended to produce short- to midterm irradiance forecasts for solar array locations. The irradiance forecasts are translated into power with uncertainties quantified using an analog ensemble approach and are provided to the industry partners for real-time decision-making. The Sun4Cast system ran operationally throughout 2015 and results were assessed. This paper analyzes the collaborative design process, discusses the project results, and provides recommendations for best-practice solar forecasting.

A Recent Invasion Wave Of Deep Learning In Solar Power Forecasting Techniques Using Ann

2021 ◽  
Author(s):  
Tuan-Anh Nguyen ◽  
Manh-Hai Pham ◽  
Trung-Kien Duong ◽  
Minh-Phap Vu
Keyword(s):  
Deep Learning ◽  
Solar Power ◽  
Invasion Wave ◽  
Solar Power Forecasting ◽  
Power Forecasting ◽  
Forecasting Techniques
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