Conceptualizing good practices: adding efficiency by using intelligent systems and processes

Panel: 4. Undertaking high impact actions: The role of technology and systems optimisation

This is a peer-reviewed paper.

Author:
Ville Rantanen, Aalto University, Finland

Abstract

Intelligent systems increase efficiency on many levels. Planning solutions affect need for transportation, heating, cooling and lighting of buildings. Design of buildings and transportation infrastructure affects functionality and flexibility. Solutions and behavioural influencing in business, work and social organization make better use of existing infrastructure and human or material resources. New technology reduces physical mobility, energy and materials consumption; new devices and machines, power plants and distribution networks become more energy-efficient.

Intelligent, flexible process aims to alter continuously due to the demand; in which experience and information are gained and used to improve the organization and implemented system. This will optimize control by changing output in real time according to real needs.

Intelligent systems are usually designed for a specific purpose in a narrow subsector. The solutions, policies and practices developed are usually recognized and used in the same zone only. Copying existing models can lead to unsatisfying results as situations and background conditions may differ significantly. By conceptualizing results to a more abstract level, good solutions found in different fields can be better utilized in an interdisciplinary way.

We will formulate methods for conceptualizing good practices and solutions of intelligent processes and systems, and show examples of case studies covering intelligent systems such as software automation, modularisation and prototyping. More specifically, potential decline in electricity use will be discussed in this context. This includes policies as well as technical solutions such as intelligent systems for lighting in which the lights have the ability of dimming as required based on a learning artificial intelligence system. The proposed actions allow a significant potential in saving energy in residential and commercial buildings, which will be emphasized in the life-cycle costs.