Highly Accurate Daily Solar Radiation Forecasting using SW-SVR for Hybrid Power Plant in Indonesia

Aji, Prasetyo; Wakamori, Kazumasa; Mineno, Hiroshi

doi:10.1109/icnere.2018.8642593

Cited by 2 publications

(2 citation statements)

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“…Outlier values can affect the accuracy of the results and increase the RMSE value. Meanwhile, the MAE values based on Table 1 are generally smaller than the RMSE values because the outlier distribution has little effect on the accuracy and error values based on MAE 51) . The wind power prediction with LSTM method shows 1.9% accuracy improvement in average with one additional weather data.…”

Section: Resultsmentioning

confidence: 97%

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Short-Term Wind Forecasting with Weather Data using Deep Learning - Case Study in Baron Techno Park

Halidah,

Hesty,

Aji

et al. 2023

Evergreen

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In a microgrid with small-scale renewable sources, the unpredictable and highly variable nature of wind necessitates the adoption of reliable wind forecasting technologies. This study employs artificial neural networks (ANNs), specifically the Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP), which are classified as Deep Learning (DL) networks. These models integrate diverse weather data, such as wind speed, temperature, humidity, and atmospheric pressure, derived from actual measurements collected in Baron Techno Park, an isolated microgrid situated in the coastal region of Yogyakarta, Indonesia. For various scenarios, the root-mean-square error (RMSE) and mean absolute error (MAE) performances of the proposed ANN-based multivariable model are given and contrasted. Furthermore, it examines the impact of incorporating multiple local variables in contrast to solely relying on wind power, comparing against the persistence method. The findings reveal that the model incorporating a comprehensive set of weather data as inputs attains the lowest RMSE and MAE values. It is also can be concluded that additional weather data, even though they show almost no correlation to wind power in Baron Techno Park can improve short-term wind power prediction, with an improvement of 2.3% for every addition of weather parameter.

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Section: Resultsmentioning

confidence: 97%

“…The methods are RMSE and MAE. The Root Mean Square Error (RMSE) is a method of evaluating metrics that uses a standard deviation from a prediction of errors or residuals 51) . Residual is the difference between the predicted value and the actual value.…”

Section: Evaluation Methodsmentioning

confidence: 99%