Research Article A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting Altaf Hussain , 1 Zulfiqar Ahmad Khan , 1 Tanveer Hussain , 1 Fath U Min Ullah , 1 Seungmin Rho , 2 and Sung Wook Baik 1 1 Sejong University, Seoul 143-747, Republic of Korea 2 Department of Industrial Security, Chung-Ang University, Seoul 06974, Republic of Korea Correspondence should be addressed to Sung Wook Baik; sbaik@sejong.ac.kr Received 13 May 2022; Revised 7 July 2022; Accepted 15 July 2022; Published 5 October 2022 Academic Editor: Chun Wei Copyright © 2022 Altaf Hussain et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. For efficient energy distribution, microgrids (MG) provide significant assistance to main grids and act as a bridge between the power generation and consumption. Renewable energy generation resources, particularly photovoltaics (PVs), are considered as a clean source of energy but are highly complex, volatile, and intermittent in nature making their forecasting challenging. us, a reliable, optimized, and a robust forecasting method deployed at MG objectifies these challenges by providing accurate renewable energy production forecasting and establishing a precise power generation and consumption matching at MG. Furthermore, it ensures effective planning, operation, and acquisition from the main grid in the case of superior or inferior amounts of energy, respectively. erefore, in this work, we develop an end-to-end hybrid network for automatic PV power forecasting, comprising three basic steps. Firstly, data preprocessing is performed to normalize, remove the outliers, and deal with the missing values prominently. Next, the temporal features are extracted using deep sequential modelling schemes, followed by the extraction of spatial features via convolutional neural networks. ese features are then fed to fully connected layers for optimal PV power forecasting. In the third step, the proposed model is evaluated on publicly available PV power generation datasets, where its performance reveals lower error rates when compared to state-of-the-art methods. 1. Introduction Photovoltaic (PV) power generation is one of the easiest-to- access, low-cost, and most promising sources of renewable energy. When the energy demands rise in the developing country, the PV power generation annually increases; therefore, it mitigates the global energy and climatic change crisis [1]. According to the Global Future Report, by 2050, the PV generation capacity will reach 8000 GW [2]. How- ever, different atmospheric variables such as temperature, solar irradiance, humidity, and cloud properties cause sig- nificant uncertainty in integrating PVs to microgrid (MG) [3–7]. In contrast, an effective PV power forecasting model greatly improves solar power utilization [8–10]. erefore, efficient forecasting models in the utility grid will operate the power grid economically and transfer the required energy to the end-users [11, 12]. Over the years, for efficient energy management and distribution, MG has played an important role in ensuring reliability, two-way power flow, self-healing, and demand response [6]. Although MG offers several advantages, due to the volatile and intermittent nature of PV power, integrating a larger portion of renewable energy into existing power generating systems creates several challenges, such as load and demand mismatch, poor scheduling, op- eration, penalties enforced by customers, and fluctuations in the load connected to the power systems. To tackle these challenges, integrating an intelligent forecasting model into the MG greatly reduces the aforementioned problems. Forecasting PV power belongs to the time series (TS) forecasting problem which are divided into univariate and multivariate forecasting [13]. Based on the time horizon, these methods are divided into three types, such as long- Hindawi Complexity Volume 2022, Article ID 7040601, 12 pages https://doi.org/10.1155/2022/7040601