Abstract
Quantitative assessment of regulation capacity of a single electric heating load is an important prerequisite for mining the regulation potential of electric heating load. In order to effectively enhance the evaluation accuracy, a Sparrow Search Algorithm (SSA) optimized Dynamic BP Neural Network (DBP) method is raised to evaluate the regulation capacity of each individual electric heating load. The SSA can dynamically choose the amount of DBP hidden layer nodes according to the different evaluation accuracy, and the DBP was trained to output the evaluation results. The simulation example verifies that, compared with the first-order equivalent thermal parameters (ETP) model, the proposed evaluation method can more truly reflect the regulation ability of a single electric heating load and improve the credibility of the evaluation results.
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