Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 1 de 1
Add filters

Document Type
Year range
4th IEEE International Conference on Cognitive Machine Intelligence, CogMI 2022 ; : 91-100, 2022.
Article in English | Scopus | ID: covidwho-2271371


Accurate energy consumption prediction is critical for proper resource allocation, meeting energy demand, and energy supply security. This work aims at developing a methodology for accurately modeling and predicting electricity consumption during abnormal long-lasting events, such as COVID-19 pandemic, which considerably affect consumption patterns in different types of premises. The proposed methodology involves three steps: (A) selects among multiple models the most accurate one in energy consumption prediction under normal conditions, (B) uses the selected model to analyze the impact of a specific abnormal event on energy consumption for various classes of premises, and (C) investigates which features contribute most to energy consumption prediction for abnormal conditions and which features can be added to improve such predictions.We use COVID-19 as a case study with datasets obtained from Fort Collins Utilities, which contain energy consumption data for residential and different sizes of commercial and industrial premises in the city of Fort Collins, Colorado, USA. We also use temperature records from NOAA and COVID-19 public orders from Larimer County.We validate the methodology by demonstrating that the methodology can help design a model suited for the pandemic situation using representative features, and as a result, accurately predict the energy consumption. Our results show that the MLP model selected by our methodology performs better than the other models even when they all use the COVID-related features. We also demonstrate that the methodology can help measure the impacts of the pandemic on the energy consumption. © 2022 IEEE.