# Exploiting Deep Learning for Wind Power Forecasting Based on Big Data Analytics
## 1 Introduction  
Due to the industrial revolution, power demand has increased and fossil fuels are used extensively, resulting in an alarming energy crisis [1]. To mitigate the energy crisis, regulative acts that encourage the utilization of renewable energy are promoted worldwide. Wind power has attracted a lot of attention as a Renewable Energy Sources (RES) recently. Wind power has gained popularity due to its characteristics of wide availability, low investment cost [2] and no carbon emission. Wind power helps in reducing environmental pollution [3]. It is introduced worldwide as a way to reduce greenhouse gas emission. Moreover, replacing thermal generation with wind generation leads to a fuel cost saving as wind has zero fuel costs. According to the Global Wind Energy Council [4], the cumulative capacity of wind power reached 486 GW across the global market in 2016. Wind power is expected to significantlyexpand, leading to an overall zero emission power system [5; 6]. The U.S. Department of Energy Target of Renewable Integration is responsible for providing 20% of the total energy through wind, by the year 2030 [7]. In this regard, the Independent System Operators (ISOs) are producing significant wind power and increasing their wind generation. Wind power is majorly affected by meteorological conditions, especially wind speed. Wind power exhibits strongly volatile and intermittent behavior, resulting in uncertain power output. This uncertainty significantly affects the quality of power system operations, such as distribution, dispatching, peak load management [8], etc. The greatest challenge of adapting wind power on a large scale is the control of its uncertain output. The effective solution to this issue is the correct estimate of future wind power. The correct Wind Power Forecasting (WPF) helps in improving the operation scheduling of power systems. The operating schedule for backup generators and storage systems are optimized based on the accurate WPF. The accuracy of WPF determines the amount of cost curtailment for power generation [9]. A 1% improvement in WPF accuracy results in 0.06% reduction in generation system's cost. An accurate WPF results in approximately $6 million cost saving in a large scale power system with 30% wind penetration level [10]. It is acknowledged widely that accurate WPF significantly reduces the risks of incorporating wind power in power supply systems [11]. Generally, the WPF results are in the deterministic form (i.e., point forecast). Reducing the forecasting errors of WPF is the focus of many researchers [12]. A point forecast is the estimated value of future wind energy. However, wind power is a random variable having a Probability Density Function (PDF), and point forecasts are unable to capture the uncertainty of this random variable. This is the limitation of the point forecasts. Therefore, point forecasts have limited use in stability and security analysis of power systems. To overcome the limitation of point forecasts, deep learning methods are widely used in the field of WPF. Deep Neural Networks (DNN) have the inherent property of automatic modeling of the wind power characteristics [13]. The energy data collected on high granularity proved to be a useful resource for wind power predictive analytics [14]. Recently, big data driven models show significant accuracy in wind power forecasting [15; 16]. Deep Neural Networks (DNNs) model the big data with good accuracy [17; 18]. Micro Grids (MGs) are Distributed Energy Sources (DERs). MGs are categorized into two categories: stand-alone and grid-connected MGs. MGs can either have RES generation (wind power, photovoltaic, hydro power, etc.) or fossil fuel based generation, or both. In this research work, a grid-connected MG is considered that has a Wind Power Plant (WPP). In the case of unequal demand and generation in MG, energy is traded with the Smart Grid (SG). Excessive wind power is sold to the SG in exchange for a subsidiary. To fulfill demand greater than wind generation, required energy is purchased from SG.