Predicting Wind Speed Using Optimized Long Short-Term Memory Neural Network

Mohammad Ehteram (), Akram Seifi () and Fatemeh Barzegari Banadkooki ()
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Mohammad Ehteram: Semnan University, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering
Akram Seifi: Vali-e-Asr University of Rafsanjan, Department of Water Science and Engineering, College of Agriculture
Fatemeh Barzegari Banadkooki: Payame Noor University, Agricultural Department

Chapter Chapter 17 in Application of Machine Learning Models in Agricultural and Meteorological Sciences, 2023, pp 175-186 from Springer

Abstract: Abstract Predicting wind speed is an important aspect of energy management. We used optimized long short-term memory (LSTM) to predict wind speed at different stations. LSTM parameters were adjusted using sunflower optimization (SUNO), crow optimization algorithm (COA), and particle swarm optimization (PSO). We used lagged wind speed values as inputs to the models. The best input combination was determined using the person correlation method. Based on the performance of the models, the optimized LSTM models outperformed the standalone models. This study can be useful if modelers cannot access all input data. The results also indicated that each optimization algorithm provided different accuracies depending on its advanced operators.

Keywords: Wind speed; Energy management; LSTM model; Optimization algorithms (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-9733-4_17

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DOI: 10.1007/978-981-19-9733-4_17

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