Comparison of deep learning models for multivariate prediction of time series wind power generation and temperature. (February 2020)

• Record Type: Journal Article
• Authors: Mishra, Sambeet; Bordin, Chiara; Taharaguchi, Kota; Palu, Ivo
• Is Part Of: Energy reports. Volume 6(2020)Supplement 3
• Page Start: 273
• Page End: 286
• Publication Date: 2020-02
• Subjects: Unsupervised machine learning -- Multi-variate prediction -- Short and long term prediction -- Wind power forecasting -- Time series to frequency transformation -- Performance evaluation and comparison; Power resources -- Periodicals; Energy industries -- Periodicals; Power resources; Periodicals; Electronic journals; 621.04205
• DOI: 10.1016/j.egyr.2019.11.009
• Languages: English
• ISSNs: 2352-4847

Abstract

Abstract: Wind power experienced a substantial growth over the past decade especially because it has been seen as one of the best ways towards meeting climate change and emissions targets by many countries. Since wind power is not fully dispatchable, the accuracy of wind forecasts is a key element for the electric system operators, as it strongly affects the decision-making processes. The planning horizon can be short term (1 -3 months) and long-term (6–12 months) depending on the process. The objective of this paper is to conduct a performance comparison of five deep learning models each combined with three types of data pre-processing and used for short term and long-term multi-variate predictions. The input data are time series of the wind power capacity factor and the temperature. In addition, this paper sets out to demonstrate and review the state-of-the-art deep learning models for prediction with a secondary objective to present the reader a reference point to better understand which model to choose and what factors are significant. The first contribution of this paper is to apply, assess and compare a selection of the novel and cutting-edge deep learning models for multi-variate prediction. Multi-variate predication is achieved through a proposed multiple input and multiple output (MIMO) architecture. Compared to traditional prediction models, machine learning techniques have the advantage of generalization. Among various techniques deep learning is particularly getting more attention due to the applicability to various dataset such as numerical and character. This investigation focuses on five models — Deep Feed Forward (DFF), Deep Convolutional Network (DCN), Recurrent Neural Network (RNN), Attention mechanism (Attention) and Long Short-Term Memory Networks (LSTM). The second contribution is to propose a novel approach to transform the time series dataset to signal for input and reconstruct the model predictions through inverse transformation, by means of the so-called discrete wavelet transformation and fast Fourier transformation. The different models are assessed also by comparing their performance with and without the input dataset manipulation through wavelet and FFT transformation. Beyond that, the model performances are outlined in detail, to give the reader an overview of the models to choose from for short-term or long-term prediction. The results demonstrate that the Attention and DCN perform best with Wavelet or FFT signal, whereas some other models perform better with no data preprocessing. Highlights: Multivariate short and long term (1, 3, 6 and 12 months) prediction of wind power capacity factor and temperature using deep learning models with input of five years of historical data set. Comparative investigation of five deep learning models using four error metrics such as average gap, mean square error, root mean square error and root mean squared logarithmic error. Introduction and comparison of data pre-processing and post-processing techniques by time series to frequency domain transformation, using fast Fourier transformation (FFT) and discrete wavelet transformation.