DYNAMIC ENVIRONMENTAL INDICATORS FOR SMART HOMES, ACTA UNIVERSITATIS OULUENSIS C Technica 599
|Kustantaja:||Oulun yliopisto|| |
|Painos:||Osajulkaisuväitöskirjan yhteenveto-osa|| |
|Sijainti:||Print Tietotalo|| |
|Tekijät:||LOUIS JEAN-NICOLAS|| |
Achieving the objective of a decarbonised economy by 2050 will require massive efforts in the
energy sector. Emissions from residential houses will have to be almost completely cut, by around
90% by 2050. Home automation is a potential tool for achieving this goal. However, the
environmental and economic benefits of automation technologies first need to be assessed.
This thesis evaluates the impact of home automation for electricity management in the
residential sector using environmental and economic indicators. To this end, a life cycle
assessment was performed to evaluate the impacts of the manufacturing, use and disposal phases.
The influences of end-user behaviour, household size and multiple levels of technological
deployment were also investigated. A Markov chain simulation tool, built on the MatLab
platform, was developed to assess all possible combinations of impacting factors. Dynamic
environmental indicators were developed based on the ReCiPe method for aggregating the
impacts of processes. All these indicators were then combined to form a single index based on
multi-criteria acceptability analysis.
The results suggest that home automation can decrease peak load, but that overall electricity
consumption may increase due to electricity use by the actual automation system. The effect of
home automation was more noticeable in larger households than in one-person households. In
addition, use of dynamic environmental indicators proved more relevant than fixed indicators to
represent the environmental impact of home automation. Within the life cycle of automation
technology, the manufacturing phase had the highest impact, but most of the CO2 emissions
originated from the use phase. In conclusion, the most important environmental benefit of home
automation is reducing CO2 emissions during peak time by load shifting.