A Consumption Analysis Study And Prognosis Of PT PLN IP UP Payo Selincah Electric Power Savings

Abstract

Energy consumption efficiency is a critical factor in optimizing power system performance, particularly in large-scale industrial and utility operations. PT PLN IP UP Payo Selincah, a key electricity provider, requires a comprehensive analysis of its power consumption patterns to identify potential energy savings and enhance operational efficiency. This study aims to analyze historical electricity consumption data and develop a prognosis model for energy savings at PT PLN IP UP Payo Selincah. The research methodology involves data collection from operational records, followed by statistical and computational analysis using machine learning and time-series forecasting techniques. The study evaluates key consumption parameters, including load profiles, peak demand periods, and efficiency losses, to determine areas where energy-saving measures can be implemented. Advanced predictive models, such as ARIMA and artificial neural networks (ANNs), are employed to forecast future consumption trends and assess the potential impact of optimization strategies. Findings from the analysis indicate significant opportunities for reducing energy wastage through improved load management, power factor correction, and the integration of renewable energy sources. The prognosis model provides insights into expected future consumption patterns, allowing PLN to implement proactive energy-saving policies. This research contributes to enhancing the sustainability of PT PLN IP UP Payo Selincah's operations while aligning with national energy conservation objectives.