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Energy has been obtained as one of the key inputs for a country's economic growth and social development. Analysis and modeling of industrial energy are currently a time-insertion process because more and more energy is consumed for economic growth in a smart factory. This study aims to present and analyse the predictive models of the data-driven system to be used by appliances and find out the most significant product item. With repeated cross-validation, three statistical models were trained and tested in a test set: 1) General Linear Regression Model (GLM), 2) Support Vector Machine (SVM), and 3) boosting Tree (BT). The performance of prediction models measured by R2 error, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Coefficient of Variation (CV). The best model from the study is the Support Vector Machine (SVM) that has been able to provide R2 of 0.86 for the training data set and 0.85 for the testing data set with a low coefficient of variation, and the most significant product of this smart factory is Skelp.

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.

This paper investigated an innovative prediction approach using the bidirectional long short-term memory (BiLSTM) model for the forecasting of solar radiation in the smart farm in Naju, South Jeolla Province. Accurate solar radiation prediction holds substantial significance in facilitating efficient solar power generation, significantly impacting agricultural sustainability and resource utilization. This study rigorously assessed the effectiveness of various predictive models for forecasting solar radiation, a crucial factor in optimizing solar energy utilization in greenhouse settings. The variety of statistical and machine learning models aims to improve the accuracy of solar radiation prediction, which is crucial for precise energy management and cultivation practices. An hour-ahead mean absolute error (MAE) of 13.53 demonstrated the proposed BiLSTM model's impressive forecasting capabilities. The comparative analysis among individual models then revealed that the LSTM model achieves an MAE of 15.23, extreme gradient boosting (XGBoost) at 24.64, and autoregressive integrated moving average (ARIMA) at 28.52. This exploration underscored the important role of accurate solar radiation forecasting, specifically highlighting the effectiveness of the BiLSTM model in optimizing solar power generation within greenhouse environments. Its importance extended to the promotion of sustainable agricultural practices and the improvement of resource-efficient energy control strategies.

To monitor and control rapid changes in climate and environment, which are crucial issues in the agricultural field, we propose an advanced architecture including environment sensing technology, a network, information processing methods, and software applications. This paper describes a greenhouse environment middleware approach that can support the proper services by analyzing agricultural requirements and crop and environmental conditions through monitoring. We have designed and implemented the Context-based Adaptive Middleware (CAM) to address greenhouse requirements and developed and tested an experimental test bed to assess the initial middleware approach.