Smart Energy @ Home - a project that lives by data
Smart Energy @ Home -‐ a project that lives by data An exciting project called Smart Energy @ Home is underway in the Danish municipality of Middelfart, where access to data -‐ including public data -‐ plays a key role. The focus of the project is to develop scalable methods to help homeowners save energy without sacrificing comfort. This is done by examining how much energy 2-‐300 homes can save by installing intelligent energy management and receive remote counseling. Background Denmark has a political target that electricity and heat in 2035 will be produced with 100 % renewable energy. In this context, it is a base assumption that the total requirement for heating -‐ in spite of new buildings -‐ must be cut in half by 2050. But even if we look twenty years ahead from now more than 70% of the building stock will consist of homes that are already built today. These buildings have a much higher consumption energy than the buildings that we build today and that will be built in the future. It is therefore in the established housing the largest energy savings are to be realized in order to achieve the goal of full phase-‐out of fossil fuels.
The Danish Building Research Institute has estimated that 200 billion Danish kroner must be invested to halve heat consumption in existing buildings. To nudge homeowners volunteer to make the necessary investments in order to halve energy consumption for heating there is a need for very active and educational counseling and a wide range of credible energy efficiency services offerings. Against this background, the project goal is to develop and demonstrate new concepts and offers to homeowners which proves that smart energy in the home for the measurement and control of heating systems in combination with a resource efficient customer dialogue and counseling to homeowners provides: • Verifiable and sustained "automatic" savings and • Activate homeowners and increases their desire to change consumption behavior and implement new energy investments. Intelligent energy management The home automation system used in the project is called PassivLiving and is developed by PassivSystems, a leader in energy optimization of private homes. PassivLiving lowers the temperature in the house when the occupants are not at home during the day, when they are on vacation, or when they go to bed. And the system also ensures that the temperature is turned up again when needed. In contrast to standard time control of heating systems the residents do not have to guess how many hours the heating systems must be on for their house to reach the desired temperature, when they get up in the morning and come home in the afternoon. This adjusts PassivLiving itself, so all that’s needed is to specify the temperature desired in the house at which time. PassivLiving is being installed in 2-‐300 houses in Middelfart municipality. Remote counseling The remote counseling will try out new IT-‐based concepts for user involvement and resource-‐efficient advice, where measurements and advanced algorithms provide energy advisors and homeowners a particularly good basis for assessing possible measures for energy optimization of the property. The goal is to make it better and cheaper than traditional energy consultancy. • Better -‐ because there is access to specific and detailed data on the condition of the building and its dynamic energy consumption. • Cheaper -‐ because there is no requirement for an an expensive consultant to inspect the property on-‐site. From data to value The diagram below illustrates the relationship between the individual homes, the various data sources and the remote counseling service in the project:
A wide range of data concerning home energy consumption are measured, including • Energy input to the home heating system (remotely read in conjunction with the relevant utility where possible) • Amount of heating water produced • Hot water consumption • The homes temperature These measurement data are supplemented by a number of other data that are relevant to the home including • Weather-‐measurements and forecasts – made available to the project by the Danish Meteorological Institute • Building and Housing Register (BBR), public data about building size, type of accommodation, historical energy consumption, etc. • Additional master data for the property, such as number of occupants and their age, already completed renovations such as window replacements, etc. -‐ This data is gathered through questionnaires or from other registries By combining these data sources much useful information can be derived about each individual property, eg • The thermal profile of the house • The efficiency of the heating system
• Key figures for heating consumption of kWh per square meter and kWh per occupant and comparison with the average for homes of similar type • Household behavior in relation to family life, housing type, etc., which can be used to consider different customized smart energy solutions to various segments of residents and types of buildings. • The heating or cooling rate for the house, in conjunction with weather data The last bullet can give specific information about which parts of the house that can benefit from forms of insulation – for example if it is determined that the house is cooling faster than usual by strong easterly winds, it appears beneficial to insulate the cavity wall or replace the windows on the east side of the house. Similarly, knowledge of the heating rate by sunlight combined with weather forecasts can be used to control heating -‐ so the heat production is turned down when there is a prospect of sunshine. The above examples provide a good illustration of the possibilities that arise from being able to combine different detailed data sources with an hourly or daily granularity. Note -‐ this is not just interesting knowledge, this is information that motivates and provides actionable knowledge to homeowners about what kind of improvements and behavioral changes that can reduce energy consumption in the home of this individual home owner. In the above example, access to public data in the form of weather reports and forecasts is critical to provide the necessary basis for cost-‐effective decision making. Similarly, there are many other public data sources such as the BBR registry and other registry information which in conjunction with easy access to home consumption data, enables the creation of new innovative greentech solutions. The ‘smart energy @ home’ project kicked off in 2012 and will run through three heating seasons until 2015. The project is made possible through a grant from Realdania -‐ a philantropic association supporting projects in the built environment – and supplemented by investment from the project partners in terms of hours and/or money. The project partners are beyond Realdania: • Middelfart Municipality, pursuing an ambitious strategy for green growth • PassivSystems provides the leading edge home automation system used • Bolius – The homeowners Knowledge Center is responsible for for the remote counseling and ongoing knowledge transfer to the participating homeowners • Danish Building Research Institute (SBi) process and analyze the data collected in the project from a research perspective. You can read more – in Danish – about smart energy @ home at www.seih.dk
Ansvarlig: Søren Peter Nielsen Publiceret: 08.01.2013 http://digitaliser.dk/resource/2432118