Visualizing energy consumption activities as a tool for making everyday life more sustainable

Abstract

social context) they are performed. We discuss the importance of relating information and feedback to households' everyday activities, in order to make it relevant to households. Through our method we discover and visualize activity patterns in a household during a given period. The method is also useful to households as a reflective tool when discussing families' daily lives in relation to energy consumption. The method gives direct feedback to households and the information is relevant since it emanates from their own reported activities.

Environmental knowledge, pro-environmental behaviour and energy savings in households: An empirical study

“…However, many of the survey participants owned multiple appliances and demonstrated high levels of appliance use and demonstrated high levels of appliance use, with obvious implications for electricity consumption. This is supported by Ellegard and Palm, who argue that increasing environmental awareness and knowledge of energy saving may not be sufficient on its own to reduce energy consumption in households [61]. One reason for this is the rapid growth in ownership of household appliances and poor levels of understanding amongst residents of how much energy each of these devices consume, even amongst those who are knowledgeable about energy and environmental issues [61].…”

Integration of Methodologies for the Evaluation of Offer Curves in Energy and Capacity Markets through Energy Efficiency and Demand Response

“…The evaluation of DR potential must be based on the knowledge of end-uses. A first approach, presented in the literature to investigate energy conservation potential, is to obtain activity patterns of customers, at the aggregate level, to identify customer activities (the so called "functional model" defined in [17]), for instance, customers requesting high amounts of energy and thus might have a significantly potential market for efficiency [34]. An alternative is the use ICT and NILM technologies, see for example [15].…”

“…Different variations on Equation (10) can be found in the literature [56,57] but they present some problems. For example: they do not consider some constraints in end-use flexibility due to electrical or service characteristics irrespective of energy price (it is not possible to switch-off or reduce demand for all the loads, for instance in the case of non-dimmable lights or washing machines, i.e., a change on switching times is possible but not a modulation of demand); load patterns or service requirements are not the same throughout the day (see Figure 3, or references [15,34]); some loads need a certain recovery period (to restore steady state service after control or to recover production schedule in the case of the industrial segment but this process is usually misunderstand and oversimplified [25,27]); the customer can apply some response previous to price peak periods but not during all the periods (for example precooling or preheating options are effective but only in periods near to peak-price response periods, i.e., E ik do not exists for none or many time periods); or the fact that changes in demand, due to substitution elasticity, involve an additional use of energy (i.e., thermal losses due to the switching of WH from service periods to 8-10 h before use). For these reasons, it seems more accurate (and complex) to apply a model for the evaluation of demand changes on each specific end-use (eu), D PDR k,eu linked with PBLM models that gives some feedback to model.…”

“…This behaviour has been taken into account together with overall demand to define approximate end-use profiles (notice that it is important to give customers and aggregators a broad portfolio of options, ranging from simple DR&EE options to more complex options). In this case, feedback from everyday activities [34] of the customer is important to refine profiles, improve DR&EE success and gain customer interest in energy concerns. Figure 4 shows the proposed end-use profiles for an average customer.…”

“…An interesting way to evaluate this response is through Physically Based Load Models (PBLM), a methodology first proposed to solve cold load pickup in [17]. This is the same idea proposed in thermal design software such as EnergyPlus or eQuest [18] but this approach is too complex to evaluate load response (EnergyPlus works high order state-space models, e.g., model order around [30][31][32][33][34][35][36][37][38][39][40] and it needs some simplification to make costs affordable and efforts feasible to engage DR. Some software, for example the toolbox BRCM is proposed in the VIRGIL platform, to simplify thermal models from EnergyPlus software, see [19].…”