Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models

Ryu, Juyeol; Lee, Bora; Park, Sungho; Hwang, Seonghyeon; Park, Hyemin; Lee, Changhyeong; Kwon, Duck-Hee

doi:10.3390/en15249403

Cited by 6 publications

(3 citation statements)

References 36 publications

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“…The Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are used as evaluation metrics to assess the accuracy of the forecasting models. The models from Ryu et al [30] encompass different variants of ARIMA and other regression techniques. Notably, our proposed LSTM approach consistently outperforms all models from Ryu et al in terms of both MAE and RMSE.…”

Section: Discussionmentioning

confidence: 99%

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Kanagarathinam,

Aruna,

Ravivarman

et al. 2023

Sustainability

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Integrating wind energy forecasting into urban city energy management systems offers significant potential for optimizing energy usage, reducing the carbon footprint, and improving overall energy efficiency. This article focuses on developing a wind power forecasting model using cutting-edge technologies to enhance urban city energy management systems. To effectively manage wind energy availability, a strategy is proposed to curtail energy consumption during periods of low wind energy availability and boost consumption during periods of high wind energy availability. For this purpose, an LSTM-based model is employed to forecast short-term wind power, leveraging a publicly available dataset. The LSTM model is trained with 27,310 instances and 10 wind energy system attributes, which were selected using the Pearson correlation feature selection method to identify crucial features. The evaluation of the LSTM-based forecasting model yields an impressive R2 score of 0.9107. The model’s performance metrics attest to its high accuracy, explaining a substantial proportion of the variance in the test data. This study not only contributes to advancing wind power forecasting, but also holds promise for sustainable urban energy management, enabling cities to make informed decisions in optimizing energy consumption and promoting a greener, more resilient future.

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Section: Discussionmentioning

confidence: 99%

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Kanagarathinam,

Aruna,

Ravivarman

et al. 2023

Sustainability

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“…For a comprehensive comparison, the proposed models are compared with the traditional ARIMA, SARIMA, SARI-MAX, and a deep learning method, the neural network autoregressive model (NNAR), to assess their forecasting performance [12]. The details for each model are as follows.…”

Section: Competitive Modelsmentioning

confidence: 99%

“…The accuracy of the suggested SVR model is compared with autoregressive (AR), autoregressive moving average (ARMA), and ARIMA models by using Akaike's information criterion. [12] suggested an SVR model for short-term wind speed modeling and forecasting based on rolling origin re-calibration. Conventional time series models such as ARIMA, seasonal ARIMA (SARIMA), and ARIMA-generalized autoregressive conditional heteroskedasticity (ARIMA-GARCH) and with weather information such as MLR, ARIMA, and SARIMA with exogenous variable (ARIMAX and SARIMAX) are also used for comparison purposes.…”

mentioning

confidence: 99%

An Adaptive Strategy for Wind Speed Forecasting Under Functional Data Horizon: A Way Toward Enhancing Clean Energy

Uzair,

Shah,

Ali

2024

IEEE Access

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An important issue in competitive energy markets is the accurate and efficient wind speed forecasting for wind power production. However, wind speed forecasting models developed for one location usually do not match the other site for various reasons like changes in terrain, different wind speed patterns, and atmospheric factors such as temperature, pressure, humidity, etc. Thus, introducing a flexible model that captures all the features is a challenging task. This paper proposes a functional data analysis (FDA) approach to forecast the site variant wind daily profiles with higher accuracy. Unlike the traditional methods, the FDA is more attractive as it forecasts a complete daily profile, and thus, forecasts can be obtained in the ultra-short period. To this end, the wind speed data is first filtered for extreme values. The filtered series is then divided into deterministic (Component-I) and stochastic (Component-II) components. Component-I is modeled and forecasted based on the generalized additive modeling technique. On the other hand, Component-II is modeled and forecasted using functional models such as functional autoregressive (FAR) and FAR with explanatory variables (FARX). For comparison purposes, forecasts from the traditional univariate autoregressive integrated moving average (ARIMA), seasonal ARIMA (SARIMA), SARIMA with exogenous information (SARIMAX), and neural network autoregressive (NNAR) models are also obtained. For empirical analysis, the wind speed data are obtained from the NASA power project for the site Canada located in Durham, England, and one-day-ahead out-of-sample forecasts are obtained for a complete year. The forecasting performance of different models is assessed through different accuracy measures, namely mean error, root mean squared error, mean absolute error, and mean absolute standard error. The results indicate that the functional models outperform the classical ARIMA, SARIMA, SARIMAX, and a deep learning model, NNAR. Within the functional models, the forecasting ability of the FARX is superior to FAR.

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Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique

Ali,

Aly

2024

Engineering Applications of Artificial Intelligence

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Evaluation of Weather Information for Short-Term Wind Power Forecasting with Various Types of Models

Cited by 6 publications

References 36 publications

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network

An Adaptive Strategy for Wind Speed Forecasting Under Functional Data Horizon: A Way Toward Enhancing Clean Energy

Short term wind speed forecasting using artificial and wavelet neural networks with and without wavelet filtered data based on feature selections technique

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