Abstract
Economic progress is built on the foundation of energy. In the industrial sector, smart factory energy consumption analysis and forecasts are crucial for improving energy consumption rates and also for creating profits. The importance of energy analysis and forecasting in an industrial environment is increasing speedily. It is a great chance to provide a technical boost to smart factories looking to reduce energy usage and produce more profit through the control and optimization modeling. It is tough to analyze energy usage and make accurate estimations of industrial energy consumption. Consequently, this study examines monthly energy consumption to identify the discrepancy between energy usages and energy needs. It depicts the link between energy consumption, demand, and various industrial goods by pattern recognition. The correlation technique is utilized in this study to figure out the link between energy usage and the weight of various materials used in product manufacturing. Next, we use the moving average approach to calculate the monthly and weekly moving averages of energy usages. The use of data-mining techniques to estimate energy consumption rates based on production is increasingly prevalent. This study uses the autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) to compare the actual data with forecasting data curves to enhance energy utilization. The Root Mean Square Error (RMSE) performance evaluation result for ARIMA and SARIMA is 8.70 and 10.90, respectively. Eventually, the Variable Important technique determines the smart factory’s most essential product to enhance the energy utilization rate and obtain profitable items for the smart factory.
Highlights
Electricity is the strongest adaptable energy source and one of the most important infrastructure inputs for economic development
We analyze data to find out twenty-four hours of energy usage and energy demands and find out relations between different materials weight with uses and energy demand
We examined energy consumption and manufacturing data sets to determine how much energy is consumed and demanded based on the weights of the materials used in manufactured items
Summary
Electricity is the strongest adaptable energy source and one of the most important infrastructure inputs for economic development. Much energy is used for both economic and population expansion. We have to think about ways to reduce our energy consumption. Due to rapid economic expansion, global electric power consumption and other energy consumption have increased. Predicting electrical energy consumption for a country or region has become crucial [3]. Energy modeling and analysis have become a time-consuming procedure. Industrial energy consumption predictions can make a better decision to reasonably control all kinds of equipment for reducing energy consumption and make good things for industrial factories. Industrial firms recognize the need to monitor and forecast energy consumption data in conjunction with production data for profit. For improving the energy utilization of a factory, this is the best time to work as a supportive technological hand with industrial plants
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