ItemConference Paper

Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM Hybrid Model

Title
Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM Hybrid Model
Creator
Avin, Lydia Mary; Vinodha, D.; Jenefa, J.; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Swamy, C. Manjunatha
Description
The global shift to sustainable energy is increasing the demand for wind energy. Accurate forecasting becomes crucial for renewable energy systems to function effectively in terms of resource allocation, grid management, and overall reliability. The need for wind energy is growing as a result of the worlds transition to sustainable energy. For renewable energy systems to operate efficiently in terms of resource allocation, grid management, and overall reliability, accurate forecasting becomes essential. It is challenging for current forecasting methods to correctly predict the dynamic nature of wind energy demand. For utilities and grid managers, the inherent variability and unpredictability in wind energy generation pose serious issues. The forecasting models that are now in use are challenged by the variable and sporadic character of wind energy generation. This makes it more difficult to integrate wind energy into the electrical grid effectively and increases the risk of grid instability and inefficient resource utilization. This research addresses these challenges by proposing a hybrid forecasting model that integrates the strengths of Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs). By capturing both spatial and temporal dependencies in wind data, the hybrid model aims to enhance accuracy and reliability in wind energy forecasts. The precise forecasting of wind energy is made more difficult by shifting weather patterns, changing environmental factors, and shifting patterns of energy usage. Improving the forecasting models accuracy and dependability in the renewable energy industry requires addressing these difficulties. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Smart Innovation, Systems and Technologies;Volume;114 SIST;pp.41-52
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
Forecasting; Hybrid models; Wind energy
Coverage
Avin L.M., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Vinodha D., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Jenefa J., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Sambandam R.K., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Vetriveeran D., Christ (Deemed to Be University), Karnataka, Bengaluru, India; Swamy C.M., Christ (Deemed to Be University), Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 21903018; ISBN: 978-981964717-0;
Format
online
Language
English
Type
Conference paper

Citation

Avin, Lydia Mary; Vinodha, D.; Jenefa, J.; Sambandam, Rakoth Kandan; Vetriveeran, Divya; Swamy, C. Manjunatha, “Enhancing Reliability and Accuracy in Wind Energy Forecast Using CNN-LSTM Hybrid Model,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25532.

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