**1. Introduction**

Current demand for energy consumption is predicated on burning fossil fuels to provide dependable and resilient energy networks. Unquestionably, one of the most significant issues for scientists and engineers is the requirement for energy. Energy production techniques from the preceding century are now acknowledged as being inappropriate because of rising atmospheric carbon dioxide (CO2) emissions [1]. The significant contribution of non-renewable energy sources raises environmental issues since they release greenhouse gases into the atmosphere, which have detrimental effects on human health and the ecosystem. As they are generally accessible, clean, and free of pollution, renewable energy sources (RESs) have gained popularity and attention across the globe. By 2030, it is anticipated that RESs like wind, solar, hydro, geothermal, and biomass would dominate world's electricity production and surpass all other energy sources [2]. Among the renewables, photovoltaics have experienced the fastest increase over the past few decades. Currently, wind energy production exceeds that of photovoltaic (PV), although PV is more widely accessible and wind turbines require extremely specialized site conditions. As an alternative, photovoltaics have become a viable option in the fight against climate change. The process of turning light into electricity using semiconducting materials that display the

**Citation:** Qaiyum, S.; Margala, M.; Kshirsagar, P.R.; Chakrabarti, P.; Irshad, K. Energy Performance Analysis of Photovoltaic Integrated with Microgrid Data Analysis Using Deep Learning Feature Selection and Classification Techniques. *Sustainability* **2023**, *15*, 11081. https://doi.org/10.3390/su151411081

Academic Editors: Prince Winston David and Praveen Kumar B

Received: 30 May 2023 Revised: 6 July 2023 Accepted: 10 July 2023 Published: 15 July 2023

**Copyright:** © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

photovoltaic effect is known as photovoltaic (PV). The three main PV cell technologies monocrystalline silicon, polycrystalline silicon, and thin film—control the global market. Among these, thin film solar cells offer several advantages over traditional silicon-based PV cells. They can be manufactured using low-cost techniques, such as roll-to-roll deposition or printing methods, which can potentially reduce production costs and enable large-scale manufacturing. Thin film solar cells are also lightweight and flexible, making them suitable for various applications, including building-integrated photovoltaics, portable electronics, and off-grid installations. However, the efficiency of thin film solar cells is generally lower than that of silicon-based cells [3].

The amazing rise in human living standards and the resulting rise in electricity demand have led to a number of super-large-scale power system flaws that are now more obvious than ever [4]. The traditional fossil fuel-based power plants are unable to provide enough energy to keep up with the rising demand for electricity. Early in the twenty-first century, the idea of a microgrid (MG) for integrating clean renewable energy sources (RESs) was put forth. A small-scale local power system called a microgrid is made up of electric loads, control systems, and distributed energy resources (DERs). Energy storage systems (ESSs) and RERs are both used in the microgrid's power generation or DERs. A new sort of contemporary active power distribution system for the use and advancement of renewable energy is the microgrid [5]. However, microgrids make it more challenging to maintain a balance between energy production and consumption, and the incorporation of RESs complicates power grid operations.

PV plant power generation frequently experiences significant fluctuations, including voltage irregularities, reserve power flow issues, and power distribution problems. Additionally, energy users show unpredictable usage of power due to a variety of factors, including alterations in the environment and user activity. Therefore, it is important to analyze the performance of the system to provide better consumer services and to maintain a reliable and sustainable system.

Accurate forecasting of PV panels is a challenging task since it depends only on the weather conditions such as temperature, humidity, etc. [6]. Prediction can be carried out using many techniques such as physical mode, machine learning (ML), and deep learning (DL) [2]. Each prediction method has its own advantages and disadvantages. Physical methods, for instance, can anticipate the shifting patterns of the environment with high precision, but they need a lot of processing capacity since they require a massive amount of data. Physical techniques encounter unforeseen estimating errors and are inappropriate for short-term forecasting horizons, which raises further problems. Similar to this, the majority of the statistical models used to anticipate renewable energy are linear in design, which restricts their application to forecasting issues with wider time horizons. ML-based prediction provides findings that are more accurate than those produced by statistical and physical methods owing to its data mining and feature extraction capabilities. However, ML-based prediction models require shallow models as the foundation of their learning strategies. Trees, regressors, and neural networks with zero or one hidden layer are examples of common shallow patterns. Often, extensive knowledge and expertise are needed while training such shallow models. Therefore, it is frequently difficult to investigate shallow structures theoretically. Hence, shallow models have certain disadvantages in real-world applications. Lately, it has been determined that DL-based approaches to energy generation and power load forecasting performed better than ML-based approaches. Unlike ML, DL-based methods do not have problems with manually chosen feature selection, complex samples, or ineffective generalization competence [7]. Consequently, it is impossible to ignore the dynamics of renewable resource energy generation behavior. The limited use of MGs in literature work prevents real-world data from being taken into account while controlling energy distribution. For better energy management of real-world data, a complete framework is needed. Furthermore, power trading between different market players is completely ignored, and prior statistical information of uncertain renewable resource energy production was assumed to be perfectly understood. This is the driving force behind

the suggested method, which combines deep learning approaches with distributed energy management to enhance the effectiveness and dependability of the proposed system. Objectives of this work are as follows:

