论文标题
时间序列的比较分析预测家庭用电预测的方法
Comparative Analysis of Time Series Forecasting Approaches for Household Electricity Consumption Prediction
论文作者
论文摘要
由于人口和全球化的增加，对能源的需求大大增加。因此，准确的能源消耗预测已成为政府规划，减少能源浪费和能源管理系统稳定运行的基本先决条件。在这项工作中，我们介绍了对家庭能耗的时间序列预测的主要机器学习模型的比较分析。具体来说，我们使用WEKA（一种数据挖掘工具）首先在Kaggle Data Science社区提供的小时和每日家庭能源消耗数据集上应用模型。应用的模型是：多层感知器，K最近的邻居回归，支持向量回归，线性回归和高斯过程。其次，我们还在Python实施了时间序列预测模型Arima和Var，以预测有和没有天气数据的韩国家庭能源消耗。我们的结果表明，预测能源消耗预测的最佳方法是支持向量回归，然后是多层感知器和高斯过程回归。
As a result of increasing population and globalization, the demand for energy has greatly risen. Therefore, accurate energy consumption forecasting has become an essential prerequisite for government planning, reducing power wastage and stable operation of the energy management system. In this work we present a comparative analysis of major machine learning models for time series forecasting of household energy consumption. Specifically, we use Weka, a data mining tool to first apply models on hourly and daily household energy consumption datasets available from Kaggle data science community. The models applied are: Multilayer Perceptron, K Nearest Neighbor regression, Support Vector Regression, Linear Regression, and Gaussian Processes. Secondly, we also implemented time series forecasting models, ARIMA and VAR, in python to forecast household energy consumption of selected South Korean households with and without weather data. Our results show that the best methods for the forecasting of energy consumption prediction are Support Vector Regression followed by Multilayer Perceptron and Gaussian Process Regression.
