A Machine Learning-based Approach for The Prediction of Electricity Consumption
A Machine Learning-based Approach for The Prediction of Electricity Consumption
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发表时间：
2019-06
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通讯作者：
D. H. Nguyen;A. Nguyen
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作者：
D. H. Nguyen;A. Nguyen
电力供需平衡是任何电网运行和控制的最基本和最重要的问题之一。有多种方法来保证供需平衡，但在这项研究中，我们专注于一个特定的方法，以促进它，即电力消费的预测，这是广泛使用的公用事业公司或系统运营商。众所周知，这种预测是具有挑战性的，因为有很多原因，例如，不准确的天气预报，不确定的消费者的行为等，因此，电力消耗的分析和线性模型可能无法很好地处理这些问题。因此，本文提出了一种基于机器学习的方法来预测用电量，其中提出了一种改进的径向基函数神经网络（iRBF-NN），其输入是时间采样点，温度和湿度与消费。该iRBF-NN的参数寻求通过解决一个优化问题，其中使用四种类型的成本函数，并比较其性能和计算成本。最后，利用所建立的模型，以每小时的温湿度预测为基础，预测未来的用电量。最后，在东京的现实数据的仿真结果来说明所提出的方法的效率。
Balancing the power supply and demand is one of the most fundamental and important problems for the operation and control of any electric power grid. There are multiple ways to guarantee the supply-demand balance, but in this research we focus on one specific method to facilitate it namely the prediction of electricity consumption, which is widely used by utility companies or system operators. It is known that this prediction is challenging because of many reasons, for example, inexact weather forecasts, uncertain consumers’ behaviors, etc. Hence, analytical and linear models of electricity consumption might not be able to deal with such issues well. This paper therefore presents a machine learning-based approach to predict electricity consumption, in which an improved radial basis function neural network (iRBF–NN) is proposed, whose inputs are time sampling points, temperature, and humidity associated with the consumption. The parameters of this iRBF–NN are sought by solving an optimization problem where four types of cost functions are used and compared on their performances and computational costs. Afterward, the derived model is employed to predict the future electricity consumption based on the hourly forecasts of temperature and humidity. Finally, simulation results for realistic data in Tokyo are presented to illustrate the efficiency of the proposed approach.