支持向量机
随机森林
电
计算机科学
多项式回归
回归分析
线性回归
消费(社会学)
能源消耗
算法
机器学习
工程类
社会科学
电气工程
社会学
作者
Jose Mari M. Arce,Erees Queen B. Macabebe
标识
DOI:10.1109/iotais47347.2019.8980380
摘要
The demand for electricity in the Philippines has been steadily increasing with about one-third of the share going to the residential sector. Thus, there is a need to introduce energy management tools for residences to allow households to take control of their electricity consumption. This work presents a system which provides information on the power consumption of a residence through energy monitoring and forecasting. The system was deployed in a residential unit with a solar PV array and the electricity consumption was monitored for 28 days using an online cloud-server database. Moreover, different regression techniques and machine learning algorithms, such as linear and polynomial regression, support vector regression (SVR) and Random Forest, were trained and implemented to identify the model that gives the best accuracy in predicting the total electricity consumption of the residence at the end of the month. Results show that the linear and polynomial regressions produced large errors due to the nonlinear trend of the consumption data, which is attributed to the generated energy of the solar PV array. The support vector regression algorithm generated models with low percent errors in predicting the end of the month electricity consumption. Moreover, the random forest regression accurately predicted the next-day electricity consumption at 0.58 % error. However, the models generated using Random Forest are not suitable for long-term prediction.
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