Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance

Kumari, Pratima; Toshniwal, Durga

doi:10.1016/j.jclepro.2020.123285

Cited by 169 publications

(53 citation statements)

References 43 publications

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“…GB is capable of both classification and regression tasks. GB had been recently used in various fields including rain prediction [4], rainfall prediction [12], transpiration estimation [13], undrained shear strength prediction [14], concrete strength prediction [15], groundwater level prediction [16], and solar irradiation forecasting [17].…”

Section: -03mentioning

confidence: 99%

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Anwar

Pratiwi

Udhayana

2021

ASSET

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Capturing and responding to complaints from the public is an important effort to develop a good city/country. This project aims to utilize Data Mining to automatize complaints categorization. More than 35,000 complaints in Bangalore city, India, were retrieved from the “I Change My City” website (https://www.ichangemycity.com). The vector space of the complaints was created using Term Frequency–Inverse Document Frequency (TF-IDF) and the multi-class text classifications were done using Random Forest (RF) and Gradient Boosting (GB). Results showed that both RF and GB have similar performance with an accuracy of 73% on the 10-classes multi-class classification task. Result also showed that the model is highly dependent on the word usage in the complaint's description. Future research directions to increase task performance are also suggested.

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Section: -03mentioning

confidence: 99%

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Anwar

Pratiwi

Udhayana

2021

ASSET

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“…Despite the fact that several well-known metrics and procedures for forecasting performance assessment are presented in the literature, most studies are limited to assessing performance of the entire evaluation period, while some further detail at how performance varies with month or season [16,[29][30][31], but comparatively few studies provide details at the type-of-day [15] level or for specific values or ranges for K t [14,32]. This analysis is of interest, because as Figure 1 shows, the same method can yield very different results as a function of the variability of the location under analysis.…”

Section: Present Work and Scientific Contributionsmentioning

confidence: 99%

Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach

2021

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Solar energy is an economic and clean power source subject to natural variability, while energy storage might attenuate it, ultimately, effective and operationally feasible forecasting techniques for energy management are needed for better grid integration. This work presents a novel deterministic forecast method considering: irradiance pattern classification, Markov chains, fuzzy logic and an operational approach. The method developed was applied in a rolling manner for six years to a target location with no prior data to assess performance and its changes as new local data becomes available. Clearness index, diffuse fraction and irradiance hourly forecasts are analyzed on a yearly basis but also for 20 day types, and compared against smart persistence. Results show the proposed method outperforms smart persistence by ~10% for clearness index and diffuse fraction on the base case, but there are significant differences across the 20 day types analyzed, reaching up to +60% for clear days. Forecast lead time has the greatest impact in forecasting performance, which is important for any practical implementation. Seasonality in data gaps or rejected data can have a definite effect in performance assessment. A novel, comprehensive and detailed analysis framework was shown to present a better assessment of forecasters’ performance.

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“…The performance of a predictive model are generally evaluated by correlating the predicted values to the actual values. In present work, three performance evaluation metrics, including root mean square error (RMSE), mean absolute percentage error (MAPE) and mean absolute error (MAE) are utilized to analyse the performance of the proposed ANN models [24], [25]. These metrics can be described mathematically as follows:…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

Real-time estimation of COVID-19 cases using machine learning and mathematical models - The case of India

Kumari

Toshniwal

2020

2020 IEEE 15th International Conference on Industrial and Information Systems (ICIIS)

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COVID-19 pandemic has stressed out the economy and resources of major countries across the world due to its high infection and transmission rate. The count of COVID-19 cases skyrocketed in the past few days, which creates immense pressure on health officials and governments. Therefore, prediction models to determine the number of new infections are urgently required in such grave times. In the present study, a machine learning technique, namely artificial neural network (ANN) is proposed to forecast the COVID-19 outbreak in India, for the first time. Moreover, in our study, we have additionally attempted to use a mathematical curve fitting model to ascertain the performance of the proposed ANN-based machine learning model. In addition, the impact of preventive measures such as lockdown and social distancing on the spread of COVID-19 is also analyzed by estimating the growth of the epidemic under different transmission rates. Moreover, a comparison between the proposed and existing COVID-19 prediction models is also demonstrated. Intriguingly, the proposed model is found to be highly accurate in estimating the growth of COVID-19 related parameters with the lowest MAPE values (cumulative confirmed cases (3.981), daily confirmed cases (4.173) and cumulative deceased cases (4.413)). Hence, the present study can assist the health officers and administration in getting prepared with the beforehand arrangement of the required resources and medical facilities.

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Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance

Cited by 169 publications

References 43 publications

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Automatic Complaints Categorization Using Random Forest and Gradient Boosting

Short-Term Deterministic Solar Irradiance Forecasting Considering a Heuristics-Based, Operational Approach

Real-time estimation of COVID-19 cases using machine learning and mathematical models - The case of India

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