Review on Predictive Model for Forecast of Renewable Energy Output: a Case Study of National Orthopedic Hospital, Igbobi

Planning and managing renewable energy systems effectively depends on accurate predictions of the energy sources' production. The National Orthopedic Hospital in Igbobi is the subject of a particular case study in this review paper, which focuses on the creation and use of predictive models for predicting the output of renewable energy. The objective is to provide an overview of the existing literature on predictive models and assess their effectiveness in forecasting renewable energy generation. The review begins by highlighting the importance of accurate renewable energy forecasts and their implications for energy management. It then explores the methodologies used in developing predictive models, including statistical techniques, machine learning algorithms, and hybrid models. Various factors influencing renewable energy output forecasting, such as weather conditions, system characteristics, and historical data availability, are discussed. Predictive models for renewable energy production forecasting in comparable healthcare contexts are reviewed, along with case studies and other relevant literature. It examines how well various modeling techniques—such as support vector regression (SVR), autoregressive integrated moving averages (ARIMA), and artificial neural networks (ANN)—capture the intricacies of the production of renewable energy. It discusses the significance of model validation and verification techniques to ensure reliable and robust forecasting results. The findings of this review contribute to the understanding of predictive models for renewable energy output forecasting in healthcare facilities. It highlights the potential benefits of accurate forecasts, such as improved energy planning, enhanced renewable energy utilization, and cost reduction. In addition, the assessment highlights areas for future research and development needs in the creation and use of predictive models for renewable energy forecasting in comparable settings.