# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 1 Introduction  
In the past decade, the renewable energy market is growing rapidly to reduce energy costs, greenhouse gas emissions, and the consumption of fossil fuels [1]. Solar energy is considered to be one of the most promising energy alternatives due to the sustainability and availability [2]. Photovoltaic (PV) systems are the most commonly applied in solar energy utilization [3]. As shown in Figure 1, the new installed capacity in 2017 in China was 53.06 GW, which was about 25 times of the installed capacity in 2011, and accounts for half of the global newly installed capacity. In China, the domestic PV power market is undergoing a profound change with the shift to an independent power sources. Figure 1: Annual newly installed capacity of PV (2011–2017). A larger proportion of PV power in energy source structure is challenging the grid stability because of the intermittent characteristics of PV production [4]. Such unstable power production would cause control and operation problems in grids, which would lead to sudden surpluses and frequent drops in the course of grid running [5]. In order to dispatch the load reasonably and make a further dispatch plan, power output prediction for PV plants was put forward and rapidly developed in recent years [6]. With respect to time horizon of prediction, the power production forecasting of a PV plant can be grouped into two categories [7]. Long-term forecasting (from days to a month) of PV power was applied to make decisions pertaining to the installation of new PV plants and storage systems, as well as develop suitable demand response strategies [8; 9]. Short-term forecasting (from minutes to 1 day ahead) was widely used in optimizing the real-time power dispatching, electricity market transaction, and maintenance plan [10]. In the view of optimizing the dispatch plan and flattening the fluctuation of PV powers, improving the precise short-term forecasting is much more significant. It is known that the performance of a given PV plant is mainly influenced by weather conditions, especially the solar irradiance and temperature, which are intensely variable. Obviously, power output prediction of PV plants essentially based on numerical weather predicting (NWP) [11]. According to the investigating result, the accuracy of 1-h-ahead numerical weather prediction can reach 95%. However, as the prediction time horizon increases, the predict accuracy drops sharply. So the meteorological information from NWP could be accepted as the input of 1-h-ahead power forecasting. In order to enhance predict performance, research is needed to optimize the prediction method in both input data and predict algorithm. Instantaneous monitoring data--for example, solar irradiance and solar cell or air temperature--are applied in a predictive model, real-time prediction is accurate, but the real-time data are not easily accessible [12; 13; 14]. On the other hand, based on hourly solar irradiation and air temperature data provided by weather bureau, other researchers focus on improving the prediction model. A prediction method was proposed to forecast power output of PV systems based upon the weather forecasting data and support vector machines (SVM), with the mean relative error (MRE) of 8.64% [15]. A 1-h-ahead power output forecasting of a PV system was presented using a combination of wavelet transform (WT) and radial basis function neural networks (RBFNN) by incorporating the interactions of PV system with solar radiation and temperature data [16]. The mean absolute percentage error (MAPE) values obtained from the model for sun days and cloudy days can reach 2.38% and 4.08%, respectively. However, the performance for rainy days is unsatisfactory. Another technique in these forecasting models is a specific soft-computing technique known as artificial neural networks (ANNs). Power outputs of a PV plant with forecasting horizons of 1- and 2-h-ahead were predicted with several forecasting models, and in [17] models based on ANNs optimized with genetic algorithm (GA) achieved better results.