City-Scale Electricity Demand Forecasting using a Gaussian Process Model
City-Scale Electricity Demand Forecasting using a Gaussian Process Model
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DOI：
10.1109/gtsd50082.2020.9303132
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
P. Nguyen;L. Manuel
中科院分区：
文献类型：
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作者：
P. Nguyen;L. Manuel
城市环境中准确有效的电力需求预测对于制定与电力供应规划、管理和运营相关的决策至关重要。然而，由于许多不确定性来源，例如天气条件和家庭或其他需求的变化影响电力需求固有的随机和非线性特性，这项任务很复杂。由于其提供的建模灵活性和计算效率，本研究采用高斯过程模型来预测作为温度函数的能源需求。高斯过程模型是一种贝叶斯非参数回归方法，它使用具有均值和协方差函数的联合高斯分布对数据进行建模。所选的平均函数被建模为温度的多项式函数，而协方差函数被适当地选择以反映实际的数据模式。我们采用来自美国德克萨斯州奥斯汀的每日气温和电力需求的真实数据集来评估所提出的负荷预测方法的有效性。使用平均绝对误差 (MAE)、均方根误差 (RMSE)、平均绝对百分比误差 (MAPE) 和 95% 置信区间 (95% CI) 等指标来评估模型预测的准确性。进行的数值研究表明，所提出的方法有望预测能源需求。
Accurate and efficient power demand forecasting in urban settings is essential for making decisions related to planning, managing and operations in electricity supply. This task, however, is complicated due to many sources of uncertainty such as due to the variation in weather conditions and household or other needs that influence the inherent stochastic and nonlinear characteristics of electricity demand. Due to the modeling flexibility and computational efficiency afforded by it, a Gaussian process model is employed in this study for energy demand prediction as a function of temperature. A Gaussian process model is a Bayesian non-parametric regression method that models data using a joint Gaussian distribution with mean and covariance functions. The selected mean function is modeled as a polynomial function of temperature, whereas the covariance function is appropriately selected to reflect the actual data patterns. We employ real data sets of daily temperature and electricity demand from Austin, Texas, USA to assess the effectiveness of the proposed method for load forecasting. The accuracy of the model prediction is evaluated using metrics such as mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE) and 95% confidence interval (95% CI). A numerical study undertaken demonstrates that the proposed method has promise for energy demand prediction.