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Visualization and prediction of energy consumption in smart homes

机译：智能家居能源消耗的可视化和预测

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Visualization and prediction of electrical energy can play an important role in managing the energy consumption at building level. Precise modeling of energy consumption is necessary in order to reduce consumption and thus reduce carbon emission. This paper focuses on the energy consumption of appliances normally used in a low energy consumption house. The dataset considered in this paper is collected from the freely available UCI machine learning repository. This dataset contains the records of 19735 instances of 29 attributes. Firstly, this paper uses a number of visualization tools such as box plot, correlation plot, commutative curves, and Pearson correlation map to find the impact of temperature, weather and humidity on energy consumption. It is found here that temperature and weather can contribute significantly to energy consumption. Secondly, the energy consumption in a smart house is predicted using a number of regression analysis such as using support vector regression (SVR), linear regression (LR), random forest (RF), multilayer perceptron regression (MLP) and elastic net. For this, both holdout and cross validation methods are performed. Results show that among these five models, RF exhibits the highest regression score or coefficient of determination and the lowest mean absolute percentage error. Thus, RF is a good choice for reliably predicting the household energy consumption.
机译：可视化和电能预测可以在管理建筑物水平的能量消耗方面发挥重要作用。为了减少消耗，因此需要精确建模能量消耗，从而减少碳排放。本文重点介绍通常用于低能耗房屋的电器的能耗。本文考虑的数据集从自由可用的UCI机器学习存储库中收集。此数据集包含29个属性的19735个实例的记录。首先，本文使用了许多可视化工具，例如盒绘图，相关绘图，换向曲线和Pearson相关图，以找到温度，天气和湿度对能量消耗的影响。这里发现，温度和天气可以对能量消耗产生显着贡献。其次，使用许多回归分析预测智能房屋中的能量消耗，例如使用支持向量回归（SVR），线性回归（LR），随机森林（RF），多层erceptron回归（MLP）和弹性网。为此，执行HoldOut和交叉验证方法。结果表明，在这五种模型中，RF呈现出最高的回归分数或确定系数，并且最低的平均绝对百分比误差。因此，RF是可靠地预测家庭能量消耗的良好选择。

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