Solar energy is largely dependent on weather conditions, resulting in unpredictable, fluctuating, and unstable photovoltaic (PV) power outputs. Thus, accurate PV power forecasts are increasingly crucial for managing and controlling integrated energy systems. Over the years, advanced artificial neural network (ANN) models have been proposed to increase the accuracy of PV power forecasts for various geographical regions. Hence, this paper provides a state-of-the-art review of the five most popular and advanced ANN models for PV power forecasting. These include multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). First, the internal structure and operation of these models are studied. It is then followed by a brief discussion of the main factors affecting their forecasting accuracy, including forecasting horizons, meteorological conditions, and evaluation metrics. Next, an in-depth and separate analysis of standalone and hybrid models is provided. It has been determined that bidirectional GRU and LSTM, whether used as a standalone model or in a hybrid configuration, offer greater forecasting accuracy. Furthermore, hybrid and upgraded metaheuristic algorithms have demonstrated exceptional performance when applied to standalone and hybrid ANN models. Finally, this study discusses various limitations and shortcomings that may influence the practical implementation of PV power forecasting.

In recent years, the environmental and economic consequences of coal, gasoline and other conventional energy sources have been widely discussed. Currently, Pakistan's energy output is highly dependent on these resources; as a consequence, the country is facing a severe energy crisis. The government spends more than $3.7 billion annually on fossil fuel imports, which has a significant impact on an already vulnerable economy. Moreover, the country is ranked as the seventh-most impacted region by climate change, making it critical for the government to take proactive measures. Renewable energy that is both economical and sustainable will become a realistic and viable choice for satisfying Pakistan's current and future energy demands. This article provides a detailed overview of Pakistan's long-standing energy scarcity concerns and discusses the country's current renewable energy challenges and opportunities. Additionally, Pakistan's efforts in the realm of renewable energy are compared to those of other Asian nations in order to understand how these countries are advancing renewable energy sources. Numerous statistics show that despite government initiatives, the amount of power generated from renewable sources falls short of the Indicative Generation Capacity Expansion Plan’s (IGCEP’s) 2025 and 2030 targets. Achieving these aspirational targets will require competent policies, incentives, technological expertise and substantial political and financial commitments. Hence, this article also advised policymakers and city municipalities on how to enhance energy infrastructure, knowledge and the capacity for overcoming future challenges. In contrast, the development of renewable energy in Pakistan has been an extremely successful endeavour, but an effective and efficient approach is necessary to leverage the benefits of rapid progress.

The environment and the economy are negatively impacted by conventional energy sources, such as coal, gasoline, and other fossil fuels. Pakistan’s reliance on these resources has resulted in a catastrophic energy crisis. This has driven the government to make critical decisions such as early retail closures, power outages for the industrial sector, and an increase to two days a week vacations. Wind energy, accessible and affordable, will become a viable option for meeting Pakistan’s present and future energy demands. Approximately 3% of Pakistan’s land can produce nearly 132 GW of power with an installed capacity of 5 MW per km². In this study, four zones (Karachi, Thatta, Badin, and Jamshoro) in Sindh province are assessed for the feasibility of wind energy generation. The installed capacity, generator types, and detailed specifications are provided for each zone. Moreover, the wind mapping of Pakistan is presented considering the four potential zones. The zones are analyzed using annual wind speed and power output considering wind data measured at 50 m height over one year. The higher mean speed is recorded at Jamshoro compared to other zones. The analysis indicates that all four sites are suitable for large-scale wind power generation due to their energy potential.

Load frequency control (LFC) has recently gained importance due to the increasing integration of wind energy in contemporary power systems. Hence, several power system models, control techniques, and controllers have been developed to improve the efficiency, resilience, flexibility, and economic feasibility of LFC. Critical factors, such as energy systems, resources, optimization approaches, resilience, and transient stability have been studied to demonstrate the uniqueness of the proposed design. This paper examines the most recent advances in LFC techniques for wind-based power systems. Moreover, the use of classical, artificial intelligence, model predictive control, sliding mode control, cascade controllers, and other newly designed and adopted controllers in the LFC area is thoroughly examined. Statistical analysis and a comparison table are used to evaluate the advantages, disadvantages, and applications of various controllers. Finally, this paper presents a comprehensive overview of contemporary and other widely used soft computing tools for the LFC issue. This detailed literature review will assist researchers in overcoming the gap between current progress, application, limitations, and future developments of wind energy in LFC.