Nowadays, photovoltaics (PV) has gained popularity among other renewable energy sources because of its excellent features. However, the instability of the system’s output has become a critical problem due to the high PV penetration into the existing distribution system. Hence, it is essential to have an accurate PV power output forecast to integrate more PV systems into the grid and to facilitate energy management further. In this regard, this paper proposes a stacked ensemble algorithm (Stack-ETR) to forecast PV output power one day ahead, utilizing three machine learning (ML) algorithms, namely, random forest regressor (RFR), extreme gradient boosting (XGBoost), and adaptive boosting (AdaBoost), as base models. In addition, an extra trees regressor (ETR) was used as a meta learner to integrate the predictions from the base models to improve the accuracy of the PV power output forecast. The proposed model was validated on three practical PV systems utilizing four years of meteorological data to provide a comprehensive evaluation. The performance of the proposed model was compared with other ensemble models, where RMSE and MAE are considered the performance metrics. The proposed Stack-ETR model surpassed the other models and reduced the RMSE by 24.49%, 40.2%, and 27.95% and MAE by 28.88%, 47.2%, and 40.88% compared to the base model ETR for thin-film (TF), monocrystalline (MC), and polycrystalline (PC) PV systems, respectively.

Demand for continuous and reliable power supply has significantly increased, especially in this Industrial Revolution 4.0 era. In this regard, adequate planning of electrical power systems considering persistent load growth, increased integration of distributed generators (DGs), optimal system operation during N-1 contingencies, and compliance to the existing system constraints are paramount. However, these issues need to be parallelly addressed for optimum distribution system planning. Consequently, the planning optimization problem would become more complex due to the various technical and operational constraints as well as the enormous search space. To address these considerations, this paper proposes a strategy to obtain one optimal solution for the distribution system expansion planning by considering N-1 system contingencies for all branches and DG optimal sizing and placement as well as fluctuations in the load profiles. In this work, a hybrid firefly algorithm and particle swarm optimization (FA-PSO) was proposed to determine the optimal solution for the expansion planning problem. The validity of the proposed method was tested on IEEE 33- and 69-bus systems. The results show that incorporating DGs with optimal sizing and location minimizes the investment and power loss cost for the 33-bus system by 42.18% and 14.63%, respectively, and for the 69-system by 31.53% and 12%, respectively. In addition, comparative studies were done with a different model from the literature to verify the robustness of the proposed method.

As global energy demands surge, the industrial sector, a key player, is undergoing a crucial transition towards sustainable practices while ensuring efficient production. The implementation of electricity peak shaving, a strategy that minimizes consumption during peak demand periods, emerges as a promising solution. This proactive approach delivers both environmental and economic benefits, such as reduced greenhouse gas emissions and lower energy costs. Recent advancements in the integration of solar photovoltaics, battery storage, and demand response programs have made peak shaving even more attractive. This integrated approach, has garnered significant attention due to its potential to optimize energy use without disrupting industrial operations, offers a path towards responsible industrial sustainability. However, widespread adoption of peak shaving in both industrial settings and across Malaysia is facing early adoption challenges. To bridge this gap, this paper presents a comprehensive systematic literature review, that examines the current state-of-the-art and strategies for peak shaving in the industrial sector. This review focuses on identifying key themes, including objectives, employed technologies, and implementation techniques. In addition, a case study on a Malaysian food manufacturing building is presented to demonstrate the versatility of hybrid solar PV and BESS systems. These systems can directly power operations, store solar energy in batteries, feed excess energy into the grid, and seamlessly switch to grid-supplied power when needed. The results of this case study showed a significant 6.9% reduction in energy costs and an 8.6% decrease in CO ₂ emissions. Leveraging insights from both literature and real-world projects, this study introduces the Industrial Peak Shaving framework to guide future research and implementation. The goal of this framework is to pave the way for broader and more socially and environmentally responsible industrial sustainability.