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Non-Intrusive Load Monitoring (NILM) increases awareness on user energy usage patterns. In this paper, an efficient and highly accurate NILM method is proposed featuring condensed representation, super-state and fusion of two deep learning based models. Condensed representation helps the two models perform more efficiently and preserve longer-term information, while super-state helps the model to learn correlations between appliances. The first model is a deep user model that learns user appliances usage patterns to predict the next appliance usage behavior based on past behaviors by capturing the dynamics of user behaviors history and appliances usage habits. The second model is a deep appliance group model that learns the characteristics of appliances with temporal and electrical information. These two models are then fused to perform NILM. The case study based on REFIT datasets demonstrates that the proposed NILM method outperforms two state-of-the-art benchmark methods.
Ce Peng; Guoying Lin; Shaopeng Zhai; Yi Ding; Guangyu He. Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model. Energies 2020, 13, 5629 .
AMA StyleCe Peng, Guoying Lin, Shaopeng Zhai, Yi Ding, Guangyu He. Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model. Energies. 2020; 13 (21):5629.
Chicago/Turabian StyleCe Peng; Guoying Lin; Shaopeng Zhai; Yi Ding; Guangyu He. 2020. "Non-Intrusive Load Monitoring via Deep Learning Based User Model and Appliance Group Model." Energies 13, no. 21: 5629.
Multi-objective unit commitment (MOUC) considers concurrently both economic and environmental objectives, then finds the best trade-off with respect to these objectives. This paper proposes a novel model for MOUC, and a decomposition coordination approach is presented to solve the model. The economic objective is to reduce the fuel cost while the environmental objective is to reduce the CO 2 emission. The MOUC model considers these objectives by minimizing the distance to the Utopian point, which avoids generating Pareto optimal solutions. The model is solved by a decomposition coordination approach, which decomposes the whole system into subsystems and performs an iterative process. During each iteration step, the tie-line power flow is updated based on the margin price in connected subsystems, then, each subsystem is solved by branch and bound method, and the result is improved during iterations as shown in case studies. Besides, as the process does not require uploading units parameters, it protects the privacy of generating companies. Numerical case studies conducted using the proposed multi-objective model are applied to illustrate the performance of the approach.
Shaopeng Zhai; Zhihua Wang; Jia Cao; Guangyu He. A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination. Applied Sciences 2019, 9, 829 .
AMA StyleShaopeng Zhai, Zhihua Wang, Jia Cao, Guangyu He. A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination. Applied Sciences. 2019; 9 (5):829.
Chicago/Turabian StyleShaopeng Zhai; Zhihua Wang; Jia Cao; Guangyu He. 2019. "A New Multi-Objective Unit Commitment Model Solved by Decomposition-Coordination." Applied Sciences 9, no. 5: 829.