Abstract
Artificial intelligence (AI) technology is rapidly evolving, and its applications in big data analytics and practices are becoming increasingly diverse. Deep learning-based time series prediction, such as LSTM, GRU, and BiLSTM, is a promising area for future big data analytics and techniques. Forecasting wind power is an important but difficult element of time series data analysis. The type of time series data used, as well as the underlying context, are the most important aspects that influence the performance and accuracy of time series data analysis and forecasting methodologies. This research examined the state-of-the-art techniques LSTM, BiLSTM, and GRU for wind power forecasting. The results reveal that BiLSTM-based modeling outperforms ordinary LSTM-based models in terms of prediction. Furthermore, based on time horizons, input characteristics, calculation time, error measurements, and other factors, this study produced a guideline for wind power forecasting process screening, allowing wind turbine/farm operators to pick the most relevant prediction approaches.
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Galphade, M., Nikam, V.B., Banerjee, B., Kiwelekar, A.W. (2023). Comparative Analysis of Wind Power Forecasting Using LSTM, BiLSTM, and GRU. In: Bhateja, V., Yang, XS., Lin, J.CW., Das, R. (eds) Evolution in Computational Intelligence. FICTA 2022. Smart Innovation, Systems and Technologies, vol 326. Springer, Singapore. https://doi.org/10.1007/978-981-19-7513-4_42
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