Non-parametric probability density forecast of an hourly peak load during a month

Abstract

The Load Serving Entity (LSE) requires, for its power procurement portfolio management, accurate peak load forecast in medium term (upto six months ahead). A complete description of the random variable, i.e., load, is provided by probability density function. Hence, we consider the problem of forecasting probability density function of hourly peak load during a month. First, we propose a non-parametric model based on the Alternating Conditional Expectation (ACE) to obtain point forecast. Then, by considering multiple scenarios of the weather variables i.e., temperature-humidity tuples, we obtain probability density forecast of the peak load. Out-of-sample testing is used to demonstrate efficacy of the proposed approach.

Electrical peak demand forecasting- A review

“…It also proposed a method to optimize the hyper-parameters in the kernel function, which was vital to improve forecast accuracy. [44] utilized Alternating Conditional Expectation (ACE) to model hourly peak load during a month. Unlike most multiple linear regression models, the seasonal and trend components in this model did not require a priori decomposition, and the non-parametric transformed functions could be obtained through ACE.…”

“…Therefore, more research efforts on the interaction between electricity usage decisions of end users and disaggregated load forecasting are needed in the future. [136], [46], [39], [42], [28], [45], [48], [41], [114], [131], [130], [32], [137], [40], [44], [138], [111], [122], [139], [ [92], [68], [21], [69], [62], [65], [58], [59], [46][60], [66], [ [45], [85], [145], [90], [92], [87], [146], [147], [139], [148], [94], [95], [149], [84], [150], [98], [151], [152], [153], [132],…”