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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.

Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.

Honey is one of the most significant ingredients in conventional food production in different regions of the world. Honey is commonly used as an ingredient in ethnic food. Beekeeping is performed in various locations as part of the local food culture and an occupation related to pollinator production. It is important to conduct beekeeping so that it generates food culture and helps regulate the regional environment in an integrated manner in preserving and improving local food culture. This study analyzes different types of environmental factors of a smart bee farm. The major goal of this study is to determine the best prediction model between the linear regression model (LM) and the support vector regression model (SVR) based on the environmental factors of a smart bee farm. The performance of prediction models is measured by R2 value, root mean squared error (RMSE), and mean absolute error (MAE). From all analysis reports, the best prediction model is the support vector regression model (SVR) with a low coefficient of variation, and the R2 values for Farm inside temperature, bee box inside temperature, and Farm inside humidity are 0.97, 0.96, and 0.44.

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In the recent past, the agricultural industry has rapidly digitalized in the form of smart farms through the broad usage of data analysis and artificial intelligence. Commonly, high operating costs in a smart farm are primarily due to inefficient energy usage. Therefore, accurate estimation of agricultural energy usage and environmental factors is considered as one of the significant tasks for crop growth control. The growth sequences of crops in agricultural environments like smart farms are related to agricultural energy usage and consumption. This study aims to develop and validate an algorithm that can interpret the crop growth rate response to environmental and solar energy factors based on machine learning, and to evaluate the algorithm’s accuracy compared to the base model. The proposed model was determined through a comparative experiment of three representative machine learning techniques, which are random forest (RF), support vector machine (SVM), and gradient boosting machine (GBM), considering the energy usage for environmental control is highly associated with the paprika crop growth. Through the experiment performance with real data gathered from a paprika smart farm in South Korea, the multi-level RF can effectively predict paprika growth with an accuracy of 0.88, considering data analysis of factors that use solar energy. As a result of the experiment with the suggested model, the growth factors such as leaf length, leaf width, and environmental factors were found. Furthermore, the proposed algorithm can contribute to the development of applications through analysis of the crop growth big data for various plants in agricultural environments such as a smart farm.

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.