## 1 Introduction  
In recent years, faced with energy depletion because of the increase in the world's population, oil price instability, and limited resources, there has been increasing interest in new renewable energies to solve this problem. In Korea, small farmhouses require low cost energy in the winter season. We focus on one of the renewable energies, like solar power and solar heat.  
Furthermore, these renewable energies can change rapidly according to the climate. Hence their continuous management is required to maintain efficient energy, which continuously requires integrated monitoring services to maintain the energy production, operational status, and utilization of renewable energy installations.  
In Korea, the New and Renewable Energy Center (NREC) of Korea Energy Management Corporation [1; 2; 3] has organized for the improvement of facility utilization by establishing a monitoring system. Moreover, it provides data for making policies, research, and projects.  
Power exchange [4] is an efficient system for managing and controlling electrical energy, which supports just the homogeneous systems and does not manage the photovoltaic (PV) power generation of different systems. Further, technical support and maintenance may be unmanaged or unusable after the maintenance period for new renewable energy-related systems [5].  
Presently, studies are conducted on various monitoring systems, such as the PV monitoring system combined with current GPS technology [6], web-based monitoring and remote control technology of the PV system [7], cloud service-based mobile monitoring service system [8], remote monitoring system based on ZigBee communication [9], GSM-based photovoltaic power monitoring system [10], smart grid monitoring system for building/home energy management [11], and smart monitoring technique for photovoltaic power systems [12].  
Although the research on monitoring systems exists, these studies are insufficient with regard to monitoring systems connected with the existing legacy systems. To solve this problem, the metamodel-based photovoltaic monitoring system (M-PVMS) [13] provides a function for different connection and manages new renewable energy generation. The M-PVMS can efficiently integrate individual systems based on standard interfaces for data interoperability and reduce costs and times of development to plug and play new devices into the existing PVMS.  
Recently, the failure of solar panels has been a critical factor in solar power generation. The M-PVMS uses solar panels with bypass diodes. This panel offers the advantage that the power generation of the normal cell is preserved even when a failure of the solar cell of the panel occurs. However, depending on the type of inverter, a fault alarm may not occur. Therefore, it is difficult for the individual owner to recognize the failure of the panel. Consequently, the owner may not recognize to reduce the amount of power generation.  
To solve this problem, we suggest (1) prediction of solar power generation of M-PVMS using the gated recurrent unit (GRU) algorithm, which is a deep learning model used to improve long short-term memory (LSTM) as an upgrade of the existing recurrent neural network (RNN) structure, and this makes it suitable for use in M-PVMS data collection environments, (2) extension of the metamodel of M-PVMS with local environmental and public data for power generation prediction, and (3) verification of predicted generation with real generation.  
This paper is organized as follows. Section 2 describes related studies. Section 3 describes how to improve the M-PVMS integrated server with redefining the metamodel for prediction of M-PVMS power generation. Section 4 describes the accumulated data analysis within the M-PVMS system, the data identification for training data set, and power prediction module of power generation with the GRU algorithm. Section 5 describes the verification of our predication model. Finally, Section 6 presents the conclusion and future work.