A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms

Abstract

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.

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms

“…This study shows the statistical relationship metrics derived from the three ML models for the energy prediction time frame [ 34 ]. The findings reveal that RF outperforms all other ML models in terms of prediction times accuracy = 0.88.…”

“…is study shows the statistical relationship metrics derived from the three ML models for the energy prediction time frame [34].…”

A Systematic Literature Review: Internet of Things on Smart Greenhouse

“…Energy use in agricultural IoT systems is very important, it is necessary to pay attention and take into account the peak times and prediction models of energy use in IoT-based greenhouses have been made. Various algorithms have been used with research and development integrated with machine learning algorithms such as artificial neural networks, support vector machines, extreme gradient boost and random forest, in research [28] it was found that random forest produces predictions with the highest accuracy compared to other algorithms of 92 %. Integrate the Multi agent System to build greenhouse energy management to optimize energy use for two seasons in one year [29].…”