Short-Term Residential Load Forecasting Based on Resident Behaviour Learning

By Weicong Kong, Zhao Yang Dong, David John Hill, Fengji Luo, Yan Xu. IEEE Transactions on Power Systems, 2018

517 citations recorded by the source corpus.

Subfields: Building and Construction, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/68798bd266b0e8c15bee46aa765e0aafd1d82e73

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Chau Yuen
2. H. Vincent Poor
3. Wayes Tushar
4. Tapan Kumar Saha
5. Haibo He
6. Danil V. Prokhorov
7. Yuanwei Liu
8. Zhiguo Ding

Citations in the corpus

• Model-Free Real-Time EV Charging Scheduling Based on Deep Reinforcement Learning (2019, background): “Long Short-Term Memory (LSTM) network is known for its strong ability to model the time dependencies of time-series data [38], [39], and has achieved promising results in smart grid applications, such as load forecasting [40], [41].”
• Internet of Things for Green Building Management: Disruptive Innovations Through Low-Cost Sensor Technology and Artificial Intelligence (2018, background): “In [16], the authors design a long- and short-term memory for load forecasting based on residential-behavior learning using recurrent neural networks, while, in [17], accurate indoor occupancy tracking within a building is implemented using multisensor fusion.”
• Electricity load forecasting: a systematic review (2020, background): “Again, an LTSM for short-term residential load forecasting based on residential meter reading [51] and resident behaviour learning [52] was studied.”