# Short-Term Forecasting of Power Production in a Large-Scale Photovoltaic Plant Based on LSTM
## 1 Introduction
The application of artificial neural networks with a single hidden layer, which the periodicity and timing of data were not taken into account, did not greatly improve the accuracy of the prediction. Some large-scale PV systems may have various types of PV arrays installed over a wide area with different tile and azimuth angles. In addition, significant diversity of photo-electric conversion efficiency may exist among different power converters. All of these can result in potentially serious errors while modeling the specific PV power forecasting system. An appropriate strategy to overcome the aforementioned problems could be considering the historical power output generated within 3 days. Unfortunately, only a few studies using both meteorological information of next hour from NWP and historical power output have been reported in the literature so far. Furthermore, analyzing the characteristic of historical data of PV plants, the time series of PV power generation are typical of periodicity and seasonality, the advantages of two aspects above, intelligent algorithm and time series analysis, should be combined. An intelligent algorithm which combines current information and historical effect is most compatible for PV plant power output prediction. Recurrent neural networks (RNN) algorithm is one of methods that can accommodate dependencies between consecutive time steps [18; 19]. The RNN architecture structures were used for modeling time series in which neurons were fully connected with cycles feeding the activation functions from previous time steps as inputs, and the relevance between historical data and current status could be described with this structure. However, on the other hand, a \"vanishing or exploding gradients problem\" coming into with this connecting structures [20]. In order to overcome the problem, recently, a new architecture for RNN has been designed called the long short-term memory (LSTM), and a novel solution which introduced'memory blocks' was proposed to solve the problem [21]. Nowadays, LSTM algorithm has been applied to various fields, including human behavior predicting, short-term residential load forecasting and renewable energy. Aiming at early detection of the risks related to mild cognitive impairment and frailty and providing meaningful interventions that prevent these risks, [22] have created a deep learning architecture based on LSTM to predict the user's next actions and to identify anomalous user behaviors. It was also applied in residential load forecasting. The LSTM-based framework was proposed to address the short-term load forecasting problem including high volatility and uncertainty for individual residential households [23]. The proposed LSTM framework generally achieves the best forecasting performance in the dataset. Similarly, LSTM algorithm has also been applied to PV power prediction. As the main influence factor of PV power generation, the solar irradiance and its accurate forecasting are prerequisites for solar PV power forecasting. Authors in [24] proposed an improved LSTM model to enhance the accuracy of day-ahead solar irradiance forecasting, and the simulation results indicated that the proposed model has high superiority in the solar irradiance forecasting, especially under extreme weather conditions. A new method for 1-h-ahead PV power forecasting using deep LSTM networks was proposed [25], which can capture abstract concepts in the PV power sequences. The proposed method gave a very small forecasting error compared to the other methods. However, this paper did not incorporate environmental parameters, such as, wind speed, air temperature. Under certain extremely weather conditions, ever-increasing number of abnormal weather manifestations that are poorly predictable can be a complicating factor in the forecast of energy production. Therefore, various weather types and other meteorological parameters were considered in this paper. Based on analysis above, a novel deep RNN models with LSTM units was proposed.