Context-Aware Smart Energy Recommender (CASER)
| Autor: | Sarah Goodwin, Pari Delir Haghighi, Paras Sitoula, Chris Ling, Dwi Rahayu |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Creative visualization
Database Energy management business.industry Computer science Smart meter 020209 energy media_common.quotation_subject 020207 software engineering Context (language use) 02 engineering and technology Energy consumption Recommender system computer.software_genre 0202 electrical engineering electronic engineering information engineering Electricity business computer Energy (signal processing) media_common |
| Zdroj: | MoMM |
| Popis: | With increasing electricity demand, implementing smart energy saving strategies in residential houses becomes more important than ever before. Real-time and context-aware recommendation systems can provide residents with useful information to monitor their energy consumption, predict future usage, and recommend shifting their load to another time period. This study aims to improve the management of residential loads at the consumer level while at the same time providing energy providers with an overview of energy usage at the household and substation levels. This includes real time, historical and predicted usage. In this paper we introduce a Context-Aware Smart Energy Recommender (CASER) that consists of a client-side mobile app and a backend web portal. The implementation uses alternative visualization techniques to provide electricity usage information and recommendations for the consumers and the energy providers. The accuracy of our context-aware prediction was evaluated using publicly available smart meter data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|