A machine learning-based framework for clustering residential electricity load profiles to enhance demand response programs

Michalakopoulos, Vasilis; Sarmas, Elissaios; Papias, Ioannis; Skaloumpakas, Panagiotis; Marinakis, Vangelis; Doukas, Haris

doi:10.1016/j.apenergy.2024.122943

Cited by 24 publications

(1 citation statement)

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“…Suggested methodology will focus on peaks and the extreme values of the univariate daily hourly loads, filling a research gap by adding the methods of cluster and extreme value analysis [5][6][7]. The developed model will extract all available information from load as well as the inherited risk and volatility pertained in peaks and lows.…”

Section: Introductionmentioning

confidence: 99%

Electricity Demand, Forecasting the Peaks: Development and Implementation of C-EVA Method

Theodorou,

Christopoulos

2024

GLCE

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Price spikes in electricity markets are very frequent, posing tremendous burden on household income and on manufacturing cost. Electricity demand (load) can be divided in two parts, energy (MWh) and peak (MW) and most of time peak is responsible for the price spikes. Literature review while devoting most of the discussion to energy, lags in the investigation of peak. In this research, a model for peak demand analysis and forecasting is developed. The model is based on a portfolio of cluster and extreme value analysis (C-EVA) methods using UIK, EDE and WSL innovations for the optimal determination of clusters and the daily peaks divulgence. C-EVA method consists of the Clustering part for optimal number of clusters determination and classification of day and month of peak, and the part of Extreme Value Analysis for computation of the statistical confidence interval for the load maxima. C-EVA after using all the currently available load maxima, estimates statistically the expected worst-case scenario for peaks of loads. Load peaks will be determined by EVA based on an estimated bimodal distribution while a signaling method will prompt the probability of extremes. The added value of the proposed method is that does not reject the extreme values as most methodologies do. Extreme Value Analysis for maxima and minima provide estimators for highest and lowest expected hourly load, while giving the confidence interval of the return level using an optimization method for the selection of a rolling time window, as the return period. It was found that distributed generation of renewables create a camel effect on the load peaks which increases sharpness. The proposed methodology solved this issue while opening the ground for future research for the role of storage, batteries as well as for virtual power plants as an integrated portfolio of renewables generation.

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Section: Introductionmentioning

confidence: 99%