scholarly journals A Short-Term Solar Forecasting Platform Using a Physics-Based Smart Persistence Model and Data Imputation Method

2021 ◽  
Author(s):  
Srinath Yelchuri ◽  
A. Rangaraj ◽  
Yu Xie ◽  
Aron Habte ◽  
Mohit Joshi ◽  
...  
Keyword(s):  
Imputation Method ◽  
Data Imputation ◽  
Solar Forecasting ◽  
Short Term ◽  
Persistence Model

Multiple imputation analysis for propensity score matching with missing causes of failure: An application to hepatocellular carcinoma data

2021 ◽  
pp. 096228022110370
Author(s):  
Seungbong Han ◽  
Kam-Wah Tsui ◽  
Hui Zhang ◽  
Gi-Ae Kim ◽  
Young-Suk Lim ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Chronic Hepatitis ◽  
Propensity Score ◽  
Propensity Score Matching ◽  
Survival Data ◽  
Observational Studies ◽  
Imputation Method ◽  
Competing Risk ◽  
Data Imputation ◽  
Causes Of Failure

Propensity score matching is widely used to determine the effects of treatments in observational studies. Competing risk survival data are common to medical research. However, there is a paucity of propensity score matching studies related to competing risk survival data with missing causes of failure. In this study, we provide guidelines for estimating the treatment effect on the cumulative incidence function when using propensity score matching on competing risk survival data with missing causes of failure. We examined the performances of different methods for imputing the data with missing causes. We then evaluated the gain from the missing cause imputation in an extensive simulation study and applied the proposed data imputation method to the data from a study on the risk of hepatocellular carcinoma in patients with chronic hepatitis B and chronic hepatitis C.

scholarly journals Multi-Horizon Forecasting of Global Horizontal Irradiance Using Online Gaussian Process Regression: A Kernel Study

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4184
Author(s):  
Hanany Tolba ◽  
Nouha Dkhili ◽  
Julien Nou ◽  
Julien Eynard ◽  
Stéphane Thil ◽  
...  
Keyword(s):  
Comparative Study ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Computation Time ◽  
Training Process ◽  
Short Term ◽  
Forecast Horizon ◽  
Forecasting Accuracy ◽  
The Comparative Study ◽  
Persistence Model

In the present paper, global horizontal irradiance (GHI) is modelled and forecasted at time horizons ranging from 30 min to 48 h, thus covering intrahour, intraday and intraweek cases, using online Gaussian process regression (OGPR) and online sparse Gaussian process regression (OSGPR). The covariance function, also known as the kernel, is a key element that deeply influences forecasting accuracy. As a consequence, a comparative study of OGPR and OSGPR models based on simple kernels or combined kernels defined as sums or products of simple kernels has been carried out. The classic persistence model is included in the comparative study. Thanks to two datasets composed of GHI measurements (45 days), we have been able to show that OGPR models based on quasiperiodic kernels outperform the persistence model as well as OGPR models based on simple kernels, including the squared exponential kernel, which is widely used for GHI forecasting. Indeed, although all OGPR models give good results when the forecast horizon is short-term, when the horizon increases, the superiority of quasiperiodic kernels becomes apparent. A simple online sparse GPR (OSGPR) approach has also been assessed. This approach gives less precise results than standard GPR, but the training computation time is decreased to a great extent. Even though the lack of data hinders the training process, the results still show the superiority of GPR models based on quasiperiodic kernels for GHI forecasting.

scholarly journals A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Sensors ◽  
2020 ◽  
Vol 20 (20) ◽  
pp. 5947
Author(s):  
Liang Zhang
Keyword(s):  
Pattern Recognition ◽  
Missing Data ◽  
Energy Systems ◽  
Building Energy ◽  
Imputation Method ◽  
Validation Dataset ◽  
Data Imputation ◽  
Validation Data ◽  
Imputation Methods ◽  
Building Energy Systems

Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important. There are two key research gaps for missing sensor data imputation in buildings: the lack of customized and automated imputation methodology, and the difficulty of the validation of data imputation methods. In this paper, a framework is developed to address these two gaps. First, a validation data generation module is developed based on pattern recognition to create a validation dataset to quantify the performance of data imputation methods. Second, a pool of data imputation methods is tested under the validation dataset to find an optimal single imputation method for each sensor, which is termed as an ensemble method. The method can reflect the specific mechanism and randomness of missing data from each sensor. The effectiveness of the framework is demonstrated by 18 sensors from a real campus building. The overall accuracy of data imputation for those sensors improves by 18.2% on average compared with the best single data imputation method.

A novel wind turbine data imputation method with multiple optimizations based on GANs

2020 ◽  
Vol 139 ◽  
pp. 106610 ◽  
Author(s):  
Fuming Qu ◽  
Jinhai Liu ◽  
Yanjuan Ma ◽  
Dong Zang ◽  
Mingrui Fu
Keyword(s):  
Wind Turbine ◽  
Imputation Method ◽  
Data Imputation

Comparison of Different Time Series Methods for Short-Term Forecasting of Wind Power Production

2010 ◽  
Author(s):  
Gong Li ◽  
Jing Shi
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Network Models ◽  
Absolute Error ◽  
Power Production ◽  
Model Parameters ◽  
Short Term ◽  
Bp Network ◽  
Persistence Model

Reliable short-term predictions of the wind power production are critical for both wind farm operations and power system management, where the time scales can vary in the order of several seconds, minutes, hours and days. This comprehensive study mainly aims to quantitatively evaluate and compare the performances of different Box & Jenkins models and backpropagation (BP) neural networks in forecasting the wind power production one-hour ahead. The data employed is the hourly power outputs of an N.E.G. Micon 900-kilowatt wind turbine, which is installed to the east of Valley City, North Dakota. For each type of Box & Jenkins models tested, the model parameters are estimated to determine the corresponding optimal models. For BP network models, different input layer sizes, hidden layer sizes, and learning rates are examined. The evaluation metrics are mean absolute error and root mean squared error. Besides, the persistence model is also employed for purpose of comparison. The results show that in general both best performing Box & Jenkins and BP models can provide better forecasts than the persistence model, while the difference between the Box & Jenkins and BP models is actually insignificant.

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