Department of Computer Science and Electronic Engineering, Sun Moon University, Asan 31460, Republic of Korea.
Division of Computer Science and Engineering, Sun Moon University, Asan 31460, Republic of Korea.
Sensors (Basel). 2022 Dec 22;23(1):82. doi: 10.3390/s23010082.
Climate change is a shift in nature yet a devastating phenomenon, mainly caused by human activities, sometimes with the intent to generate usable energy required in humankind's daily life. Addressing this alarming issue requires an urge for energy consumption evaluation. Predicting energy consumption is essential for determining what factors affect a site's energy usage and in turn, making actionable suggestions to reduce wasteful energy consumption. Recently, a rising number of researchers have applied machine learning in various fields, such as wind turbine performance prediction, energy consumption prediction, thermal behavior analysis, and more. In this research study, using data publicly made available by the Women in Data Science (WiDS) Datathon 2022 (contains data on building characteristics and information collected by sensors), after appropriate data preparation, we experimented four main machine learning methods (random forest (RF), gradient boost decision tree (GBDT), support vector regressor (SVR), and decision tree for regression (DT)). The most performant model was selected using evaluation metrics: root mean square error (RMSE) and mean absolute error (MAE). The reported results proved the robustness of the proposed concept in capturing the insight and hidden patterns in the dataset, and effectively predicting the energy usage of buildings.
气候变化是一种自然的转变，但也是一种破坏性的现象，主要是由人类活动引起的，有时是为了产生人类日常生活所需的可用能源。解决这个令人担忧的问题需要迫切评估能源消耗。预测能源消耗对于确定哪些因素影响一个地点的能源使用至关重要，从而可以提出切实可行的建议来减少浪费性的能源消耗。最近，越来越多的研究人员将机器学习应用于各种领域，例如风力涡轮机性能预测、能源消耗预测、热行为分析等。在这项研究中，我们使用了 Women in Data Science (WiDS) Datathon 2022 公开提供的数据（包含有关建筑物特征和传感器收集信息的数据），在进行适当的数据准备后，我们实验了四种主要的机器学习方法（随机森林 (RF)、梯度提升决策树 (GBDT)、支持向量回归器 (SVR) 和回归决策树 (DT)）。我们使用评估指标（均方根误差 (RMSE) 和平均绝对误差 (MAE)）选择表现最佳的模型。报告的结果证明了该概念在捕捉数据集的洞察力和隐藏模式以及有效预测建筑物能源使用方面的稳健性。
