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Short-term load forecasting is a prerequisite and basis for power system planning and operation and has received extensive attention from researchers. To address the problem of concept drift caused by changes in the distribution patterns of electricity load data, researchers have proposed regular or quantitative model update strategies to cope with the concept drift; however, this may involve a large number of invalid updates, which not only have limited improvement in model accuracy, but also insufficient model response timeliness to meet the requirements of power systems. Hence, this paper proposes a novel incremental ensemble model based on sample domain adaptation (AWS-DAIE) for adapting concept drift in a timely and accurate manner and solves the problem of inadequate training of the model due to the few concept drift samples. The main idea of AWS-DAIE is to detect concept drift on current electricity load data and train a new base predictor using Tradaboost based on cumulative weighted sampling and then dynamically adjust the weights of the ensemble model according to the performance of the model under current electricity load data. For the purposes of demonstrating the feasibility and effectiveness of the proposed AWS-DAIE algorithm, we present the experimental results of the AWS-DAIE algorithm on electricity load data from four individual households and compared with several other excellent algorithms. The experimental results demonstrated that the proposed AWS-DAIE not only can adapt to the changes of the data distribution faster, but also outperforms all compared models in terms of prediction accuracy and has good practicality.

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In order to assess the characteristics of polybrominated diphenyl ether (PBDEs) in the atmosphere of urban Jinan, eastern China, gas phase and PM2.5 were hereby sampled in July 2020. The concentration and composition characteristics of 11 PBDEs were obtained. The Human exposure was evaluated by the daily exposure calculation method. During the observation period, the average concentration of ∑11PBDEs in PM2.5 and gas phase was 214.8±31.3 pg/m3, that of low-PBDEs was 119.1±2.1 pg/m3, and that of BDE209 was 95.8±15.4 pg/m3. Among the 10 low-PBDEs, the highest single content was BDE99, followed by BDE47, accounting for 43%. PCA showed the main sources of PBDEs in the atmosphere of urban Jinan were Penta-BDEs and Deca-BDEs. The daily respiratory exposure of children were higher than that of adults, and the potential risk should be taken seriously.