Intelligent Information System for Optimizing Production Using Energy from Photovoltaic Panels Polcom Conference 2024

Abstract

Due to the high demand of electricity used by industrial machinery, energy efficiency is a fundamental priority for minimizing operational costs and allowing higher production effectiveness. Production planning based on factory gathered information or known variables paired with prognosis techniques for photovoltaic energy generation results in an energy optimized industrial cycle. All elements involved in this kinematic chain, such as the state of equipment, human-made decisions and interactions, and active power generation will result in a specific energy efficiency point, which in most cases can be improved by proper production planning. By implementing tracking of energy consumption cycles and forecasting renewable energy production, manufacturing process can be defined so that most power intensive actions consume predominantly low-cost clean energy. Thus, leading to faster scheduling based on accurate production parameters such as required volumes, maintenance schedules, down-times, and other types of factors which impact the devised tasks. This paper introduces a software implementation that incorporates a cumulus of factors to enable repeatability in the decision-making process. The paper's focus is to present the logical structure of this implementation, demonstrated through an example involving a plastic injection molding facility operating primarily in the automotive sector.