A5020753327- Energy Load and Power Forecasting
- Smart Grid Energy Management
- Energy Efficiency and Management
- Stock Market Forecasting Methods
- Smart Grid and Power Systems
- Building Energy and Comfort Optimization
- Microgrid Control and Optimization
Shanghai Electric (China)
2024
Huazhong University of Science and Technology
2020-2021
This work proposes a novel deep reinforcement learning (DRL)-based demand response algorithm for smart facilities energy management to minimize electricity costs while maintaining satisfaction index. Specifically, accommodate the characteristics of decision-making problem, long short-term memory (LSTM) units are adopted extract discriminative features from past price sequences and fed into fully connected multi-layer perceptrons (MLPs) with measured time information, then Q-network is...
The fluctuations in electricity prices and intermittency of renewable energy systems necessitate the adoption online management schemes industrial microgrids. However, it is challenging to design effective optimal rolling horizon strategies that can deliver assured optimality, subject uncertainties volatile stochastic resources. This paper presents an adaptable scheme for microgrids minimizes costs while meeting production requirements by repeatedly solving optimization problem over a moving...
Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand minimizing transaction costs. There are many approaches used load such as support vector regression (SVR), autoregressive integrated moving average (ARIMA), neural networks, but most these methods focus on single-step forecasting, whereas multistep can provide better insights optimizing resource allocation assisting decision-making process. In this work, a novel...