# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 2 Related Work
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Inputs** & **Dataset** & **Algorithms** \\ \hline Past wind power & Delaware wind farm data, American National Renewable Energy Laboratory, 2006 & Nelder-Mead simplex optimization algorithm, Bidirectional backward Extreme learning machine [1] \\ \hline Wind power, IEEE 118-bus system parameters & Wind Integration National Unsteet, National Renewable Energy Laboratory, CASIO, MSD, ISO NE, 2007-2013 & PLEXOS tool, Flexible Energy Scheduling Tool for Integrating Variable generation tool [9] \\ \hline Past hourly wind power & 66 wind power plants data, Supervisory Control And Data Acquisition (SCADA) & Vector autoregression model, Least absolute shrinkage and selection operator [24] \\ \hline Past wind power & Wind farm, Denegl, North West Ireland, June-July 2004 & Temporally local Gaussian process [25] \\ \hline \multirow{2}{*}{10-min resolution: wind speed, wind power} & Global Energy Forecasting Competition (GFECom) 2014 & Multi-model combination method: Sparse Bayesian learning, Kernel density estimation and beta distribution fitting method [24] \\ \hline \multirow{2}{*}{5-minute resolution: wind speed, wind power} & Wind power data, Australian Energy Market Operator (ALMO), Gradient boosting, Support vector machine [27] \\ \hline Wind power, wind speed & Wind farm data, Ireland and USA, August 2006, October 2008 & Hybrid deterministic-probabilistic method with Gaussian process [28] \\ \hline \multirow{2}{*}{10-min resolution: wind speed, wind power} & National Renewable Energy Laboratory, 2005-2006 & Ensemble method, Wavelet transform, Partial least squares regression, ANN [25] \\ \hline Wind speed, wind power & GFECom 2012 & ANN, Gaussian process [30] \\ \hline Past wind wind power, past weather forecast wind speed, wind direction, temperature and humidity & Wind power generation, Alberta, Canada & Improved General selection algorithm, Wavelet neural networks, Maximum memory criterion [31] \\ \hline Wind turbine parameters, wind speed, wind power & 10-min wind farm data, SCADA & K-means clustering, Bagging ANN [32] \\ \hline Wind power, weather forecasts & 5 Wind farms data, Europe & Mutual information, Deep auto-encoders, Deep belief network [33] \\ \hline Wind speed, wind power & National Renewable Energy Laboratory (NEL), 2004 & Infinite feature selection method, RNN [34] \\ \hline Day of the year, hour, wind speed, wind direction, temperature, humidity, pressure, generators out of service & MADE wind farm, ITER, Tenerife Island, Spain, January 2014-April2016 & Multi-layer perceptron with ReLU, Long short-term memory, Nonlinear autoregressive network with exogenous inputs [35] \\ \hline \multirow{2}{*}{5-min intervals past wind power} & SIWF wind farm, China, 2011-2013 & Wavelet transform, Ensemble CNN [36] \\ \hline Wind speed, wind direction, temperature, humidity, pressure & MADE wind farm, ITER, Tenerife Island, Spain & Feed Forward ANN, SELU CNN, RNN [37] \\ \hline Past consumption, solar radiation & Victorian solar dataset & Game theory model [38] \({}^{*}\) \\ \hline Historic price and load & Hourly load and price data, NYESO, PJM, AFMO, 2010, 2013, & Flexible wavelet packet transform, Nonlinear least square support vector machine, ARIMA, TV-ABC [39] \({}^{*}\) \\ \hline Historic consumption, wind power, photovoltaic power & Micro grid data, Renewable Energy Laboratory, UTNa, 2014 & Simple moving average, Central moving average [60] \({}^{*}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Overview of related work. Therefore, in this paper, a wind power prediction method is proposed which takes wavelet packet decomposed past wind power, NWP and lagged wind power data as input. The second objective of this research work is the optimal load profiling with the incorporation of wind generation. Previously, the optimal load profile is achieved by load forecasting [38], price-demand forecasting [39] or load and generation forecasting [40]. In this work, the wind generation is also considered, in addition to day-ahead demand and price.