With the liberalization of the retail market, new parties such as load aggregators are participating in the demand response (DR). Aggregated baseline load (ABL) estimation provides a basis for aggregators to quantify the total responsiveness. This paper aims to improve the ABL estimation accuracy by using Gaussian mixture model (GMM). Modeling the distribution of consumption patterns by Gaussian distributions, GMM first divides the customers into several groups. Then, support vector regression (SVR) is utilized to estimate the baseline load over each group. And the estimated loads are summed up to form the final result. We make comprehensive comparisons in the case study. The results prove that the proposed method can improve the ABL estimation accuracy. And it is better than similar day, exponential moving average, and other regression model-based estimation methods.
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