Improve Home Energy Management System by Extracting Usage Patterns From Power Usage Big Data of Homes' Appliances
| Autor: | Ashkan Sami, Ali Reza Honarvar |
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| Rok vydání: | 2018 |
| Předmět: |
Database
Computer science business.industry 020209 energy Big data 02 engineering and technology 010501 environmental sciences computer.software_genre 01 natural sciences Power usage Energy management system 0202 electrical engineering electronic engineering information engineering business computer 0105 earth and related environmental sciences |
| DOI: | 10.4018/978-1-5225-5384-7.ch007 |
| Popis: | Many researchers have focused on the reduction of electricity usage in residences because it is a significant contributor of CO2 and greenhouse gases emissions. However, electricity conservation is a tedious task for residential users due to the lack of detailed electricity usage. Home energy management systems (HEMS) are schedulers that schedule and shift demands to improve the energy consumption on behalf of a consumer based on demand response. In this chapter, valuable sequence patterns from real appliances' usage datasets are extracted in peak time and off-peak time of weekdays and weekends to get valuable insight that is applicable in the HEMS. Generated data in smart cities and smart homes are placed in the category of big data. Therefore, to extract valuable information from such data an architecture for the home and city data processing system is proposed, which considers the multi-source smart cities and homes' data and big data processing platforms. |
| Databáze: | OpenAIRE |
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