Deterministic weather forecasting models based on intelligent predictors: A survey

Jaseena, K U; Kovoor, Binsu C.

doi:10.1016/j.jksuci.2020.09.009

Cited by 21 publications

(7 citation statements)

References 50 publications

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“…In order to simulate ERCOT wind power penetration, 18 GW and 25 GW of wind power capacity are considered using the data in [25]. With a time series of wind power output, one can consider persistence as a short term wind power forecast [26,32], so the 5-by-5 min incremental changes in wind power can be assumed as the imbalance to be coped by the SFC system from wind uncertainty (note that ERCOT has a 5-min real time market). For the load component of SFC burden, the short term load forecast error accounts for a mean absolute percentage error of 1.12% (and a 1.39% root mean squared error with 0.00036 variance) [33].…”

Section: Simulation Resultsmentioning

confidence: 99%

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

et al. 2021

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It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity.

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Section: Simulation Resultsmentioning

confidence: 99%

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

et al. 2021

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“…A comparison between three different machine learning methods, k-nearest neighbours (KNN), Soil and Water Assessment Tools (SWAT), and Representative Concentration Pathway (RCP) was elaborated in [13]. A survey on weather forecasting using deterministic parameters on the basis of intelligent predictors was proposed in [14]. Convolutional LSTM was applied in [15] for forecasting weather events.…”

Section: Related Workmentioning

confidence: 99%

Classification for Big Data Driven Marine Weather Forecasting Using Machine Learning Techniques

J¹,

Radha²

2023

IJSREM

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Marine weather forecasting has raised sizeable awareness in numerous ocean related domains. With immeasurable portion of data to handle with, big data problem solving unfolds gateways for abundance of predictions. Machine Learning (ML) is an essential algorithm for big data prediction. However with the increasing size and nature of data i.e., Big Data, Marine Weather Forecasting with Big Data with minimum time, error and maximum accuracy is of major concern to be addressed. In this work, a method called, Perceptred-based Feature and Kriging Gradient Boost Classification (PF-KGBC) is introduced with big data with the objective of improving the prediction performance marine weather with high accuracy and less time consumption. The PF-KGBC method is split into two parts. They are feature selection using perceptron classifier model and classification using Kriging Ensemble extreme Gradient Boost for marine weather forecasting. With the assistance of supervised learning algorithm based on perceptron classifier that involves a functional input represented by vector of numbers belongs to particular class. Based on linear predictor function set of weights are integrated with feature vector to make essential marine weather feature vector. After feature selection process, Kriging Ensemble extreme Gradient Boost Classification is performed with the purpose of forecasting marine weather data. Here, the weak learner is combined to form strong classifier. Kriging regression estimates the dependent data variation when any of factors or values in independent data gets changed. With this accurate marine weather prediction with high accuracy and lesser time consumption is ensured. Meanwhile, we analyzed the results of the proposed PF- KGBC method compared with other conventional methods, and the running performance on the Java platform. The results show that the proposed method achieved satisfactory prediction results and improvements were observed in terms of accuracy, time and error rate considerably. Keywords: Machine Learning, Perceptron, Feature Selection, Kriging, Gradient Boost, Classification, Ensemble

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“…According to the methodology, there are three major types of weather forecasting systems namely, physical, deterministic and hybrid systems. Physical systems utilize probabilistic approaches to indicate weather event probability, Deterministic approaches produce more precise weather forecasts for a given location, and hybrid models which combine multiple individual prediction models to overcome several prediction limits (Jaseena and Kovoor, 2020). In the literature, all these approaches are applied for wind speed forecasting systems:…”

Section: Related Workmentioning

confidence: 99%

Wind Speed Prediction Based on Statistical and Deep Learning Models

Tyass

Khalili

Rafik

et al. 2023

Int. J. Renew. Energy Dev.

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Wind is a dominant source of renewable energy with a high sustainability potential. However, the intermittence and unstable nature of wind source affect the efficiency and reliability of wind energy conversion systems. The prediction of the available wind potential is also heavily flawed by its unstable nature. Thus, evaluating the wind energy trough wind speed prevision, is crucial for adapting energy production to load shifting and user demand rates. This work aims to forecast the wind speed using the statistical Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model and the Deep Neural Network model of Long Short-Term Memory (LSTM). In order to shed light on these methods, a comparative analysis is conducted to select the most appropriate model for wind speed prediction. The errors metrics, mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) are used to evaluate the effectiveness of each model and are used to select the best prediction model. Overall, the obtained results showed that LSTM model, compared to SARIMA, has shown leading performance with an average of absolute percentage error (MAPE) of 14.05%.

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Deterministic weather forecasting models based on intelligent predictors: A survey

Cited by 21 publications

References 50 publications

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards

Classification for Big Data Driven Marine Weather Forecasting Using Machine Learning Techniques

Wind Speed Prediction Based on Statistical and Deep Learning Models

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