Review of smart meter data analytics: Applications, methodologies, and challenges
The widespread popularity of smart meters enables an immense amount of fine-grained
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
electricity consumption data to be collected. Meanwhile, the deregulation of the power …
Probabilistic individual load forecasting using pinball loss guided LSTM
The installation of smart meters enables the collection of massive fine-grained electricity
consumption data and makes individual consumer level load forecasting possible …
consumption data and makes individual consumer level load forecasting possible …
Data-driven probabilistic net load forecasting with high penetration of behind-the-meter PV
Distributed renewable energy, particularly photovoltaics (PV), has expanded rapidly over the
past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers …
past decade. Distributed PV is located behind the meter and is, thus, invisible to the retailers …
Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions
A smart city requires an intelligent infrastructure to improve the quality of life with sustainable
environment for its citizens. There is an exponential demand for efficient, secure, reliable …
environment for its citizens. There is an exponential demand for efficient, secure, reliable …
Spatio-temporal two-dimensions data based customer baseline load estimation approach using LASSO regression
Customer baseline load (CBL) estimation plays a crucial role in financial settlement for
incentive-based demand response (DR). Most current CBL estimation methods utilize …
incentive-based demand response (DR). Most current CBL estimation methods utilize …
Adaptive horizontal federated learning-based demand response baseline load estimation
To improve the operational security and reliability of distribution networks, distribution
network operators will encourage customers to modify their demand profiles and then give …
network operators will encourage customers to modify their demand profiles and then give …
[HTML][HTML] Short-term household load forecasting based on Long-and Short-term Time-series network
X Guo, Y Gao, Y Li, D Zheng, D Shan - Energy Reports, 2021 - Elsevier
Focusing on the issue of significant randomness and low latitude of short-term household
electrical load data, this paper proposes a novel short-term load multi-step forecasting …
electrical load data, this paper proposes a novel short-term load multi-step forecasting …
Residential load forecasting: An online-offline deep kernel learning method
Residential load forecasting (RLF) is critical for power system operations. Different from
traditional system-level load forecasting, studying RLF faces the challenges of high …
traditional system-level load forecasting, studying RLF faces the challenges of high …
Solar PV inverter reactive power disaggregation and control setting estimation
The wide variety of inverter control settings for solar photovoltaics (PV) causes the accurate
knowledge of these settings to be difficult to obtain in practice. This paper addresses the …
knowledge of these settings to be difficult to obtain in practice. This paper addresses the …
An interpretable multivariate time-series anomaly detection method in cyber-physical systems based on adaptive mask
The high complexity and wide applications of cyber–physical systems (CPSs) pose a large
requirement on both accuracy and interpretability of the time-series anomaly detection …
requirement on both accuracy and interpretability of the time-series anomaly detection …