Sökning: WFRF:(Ahlgren Fredrik 1980 ) >
Unraveling Energy C...
Unraveling Energy Consumption Patterns : Insights Through Data Analysis and Predictive Modeling
-
- Maleki, Neda (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM)
-
- Xie, Xianwei (författare)
- Harbin Engineering University, China
-
- Musaddiq, Arslan (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM)
-
visa fler...
-
- Olsson, Tobias, 1974- (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM)
-
- Mozart, David (författare)
- Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM)
-
- Ahlgren, Fredrik, Senior Lecturer, 1980- (författare)
- Linnéuniversitetet,Sjöfartshögskolan (SJÖ),Institutionen för datavetenskap och medieteknik (DM)
-
visa färre...
-
(creator_code:org_t)
- 2023
- 2023
- Engelska.
-
Ingår i: 15th International Conference on Applied Energy.
- Relaterad länk:
-
https://lnu.diva-por... (primary) (Raw object)
-
visa fler...
-
https://urn.kb.se/re...
-
visa färre...
Abstract
Ämnesord
Stäng
- Most of the utility meters in Sweden are connected using the Internet of Things (IoT) technology. This opens new possibilities for understanding society’s energy consumption dynamics and making citizens aware of their power consumption usage. In this study, we investigate the patterns of electricity consumption using machine learning methods. We collected metered data from Kalmar Energi company, the electrical grid for Kalmar city in Sweden. In addition, we collected the Kalmar weather and electricity price data from the Swedish Meteorological and Hydrological Institute (SMHI) and Nordpool, the European leading power market, respectively. We comprehensively analyze the electricity consumption data to assess the changes in overall electricity demand during the year 2021 in the city of Kalmar. This information can be of significant benefit to other regions seeking to improve their sustainability and energy consumption practices. For analysis and energy consumption prediction, we utilize two forecasting models, i.e., Random Forest (RF) and XGBoost. RF model results show a high level of accuracy with the achieved R-squared (R2) value of 0.91 compared to XGBoost value of 0.87.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering (hsv//eng)
Nyckelord
- Energy consumption
- Machine learning
- Energy forecasting
- Internet of Things
- Electrotechnology
- Elektroteknik alt Electrical engineering
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)