Deep learning based multi-step short term wind speed forecasts with LSTM

Wind energy is one of the fastest progressing renewable energy sources. Wind speed forecasting has gained more attention as wind energy never causes air pollution and other hazards to the environment. Eco-friendliness and cost-effectiveness are the main reasons for the high demand for wind energy. Accurate and reliable prediction model for wind speed forecasting is very challenging because of its intermittent nature. Deep learning based architectures like Recurrent Neural Networks (RNN) and Long Short Term Memory (LSTM) are considered as better models for time series prediction. LSTM neural network models are more appropriate for learning long term dependencies. In this paper, an LSTM network for a multi-step-ahead short term wind speed forecasting is proposed. As lag values have importance in time series prediction problems, tests are conducted to find optimal lag value. The value which contributes minimum mean square error is selected as optimal lag value. The study of the results reveals that the proposed LSTM model is more effective and efficient with high predictive accuracy compared to Auto-Regressive Integrated Moving Average (ARIMA) model.

[1]  Qinghua Hu,et al.  Transfer learning for short-term wind speed prediction with deep neural networks , 2016 .

[2]  Doreswamy,et al.  Forecasting of ozone concentration in smart city using deep learning , 2017, 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[3]  Jianzhou Wang,et al.  Multi-step-ahead wind speed forecasting based on optimal feature selection and a modified bat algorithm with the cognition strategy , 2018 .

[4]  P. K. Katti,et al.  A comparative analysis for wind speed prediction , 2011, 2011 International Conference on Energy, Automation and Signal.

[5]  Irena Koprinska,et al.  Deep Learning for Big Data Time Series Forecasting Applied to Solar Power , 2018, SOCO-CISIS-ICEUTE.

[6]  Bernhard Sick,et al.  Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[7]  Yu-Lin He,et al.  Deep Neural Network Modeling for Big Data Weather Forecasting , 2015 .

[8]  Yong Qi,et al.  Long short-term memory neural network for network traffic prediction , 2017, 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE).

[9]  Bing Shi,et al.  Multi-Step-Ahead Prediction with Long Short Term Memory Networks and Support Vector Regression , 2018, 2018 37th Chinese Control Conference (CCC).

[10]  P. N. Sen,et al.  Wind speed prediction using statistical regression and neural network , 2008 .

[11]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.

[12]  Xiang Li,et al.  Deep learning architecture for air quality predictions , 2016, Environmental Science and Pollution Research.

[13]  Amir F. Atiya,et al.  A review and comparison of strategies for multi-step ahead time series forecasting based on the NN5 forecasting competition , 2011, Expert Syst. Appl..

[14]  Ali Ouni,et al.  Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches † , 2018, Energies.