An activity-based modelling framework for quantifying occupants’ energy consumption in residential buildings

Highlights

• An activity-based modelling framework is proposed for quantifying energy consumption in residential building.

• A systematic breakdown structure of energy use is established.

• A list of domestic energy-consuming activities classified according to their nature (shared or additive) is proposed.

• The framework integrates activities characteristics with occupants’ behaviors to simulate the energy consumption.

• The modelling framework is demonstrated through an example application for the ‘watching TV’ activity.

Abstract

The residential building is a major energy consumer and pollution source worldwide. The shift towards constructing energy-efficient buildings is impelling higher performance. In sustainable building, occupants become a major source of uncertainty in energy consumption. Yet, energy simulation tools often account for occupant behaviour through predefined fixed consumption profiles. Therefore, energy and buildings experts are in need for more precise methods for better forecasting the influence of occupants on the building performance. An activity-based framework for quantifying occupant-related energy consumption is proposed. The energy consumption is quantified per domestic activity as a function of households’ socio-demographic and economic attributes. The aggregation of such domestic activity energy consumption provides an accurate estimation of the household energy consumption per daily, monthly and annually periods. First, a literature review about residential energy consumption and the existing modelling approaches is presented. Second, a systematic breakdown structure of energy end-uses is proposed. The activity-based framework is then introduced. An application example is demonstrated together with simulation results. Finally, model’s utility is outlined and its possible implications are discussed.

Introduction

The building sector is a substantial energy consumer and environment pollutant in most countries. It accounts for important shares, ranging between 16 and 50%, of national energy consumptions worldwide [[1], [2], [3], [4]]. In order to reduce these consumptions and emissions and to promote sustainable development, authorities around the globe are thus establishing energy directives and regulations that help optimising building’s performance. Examples of these directives are the European “Energy Performance of Buildings” (or EPBD) and the latest French thermal regulation RT2012 [5]. Moreover, various energy efficiency labels and green building rating systems already exist worldwide, such as BREEAM in the U.K, LEED in the United States, and BBC-Effinergie in France [6]. Such energy labels and certifications encourage the use of best practices and the development of energy efficiency solutions that go beyond the minimum requirements stipulated by standards and regulations. As a result of such norms and labels, building actors are tending progressively to construct energy-efficient and green buildings. This is also accompanied with new market expectations such as the “energy performance contracts” that impel constructors to deliver energy-efficient buildings and to guarantee their performance level for a number of years [7]. As a result, a better comprehension and integration of building performance determinants into the design of buildings has become essential. At the same time and due to the deployment of smart meter, providing a solution for visualizing real time energy consumption is now a legal requirement. A better comprehension of such occupants’ behaviours is a promising way to engage occupants towards a reduction of their energy consumptions by the mean of nudges dissemination.

The energy performance of a building is governed by various parameters, such as its physical characteristics, its external environment, its internal services systems and equipments, and most importantly the behaviour of its occupants [[8], [9], [10]]. Industrial energy simulation tools, such as Energy Plus, eQUEST, ESP-r and TRNSYS, focus primarily on the structural behaviour of buildings and their relations to specific environmental conditions while taking insufficiently the role of the occupants into account [11,12]. This simplification of occupants’ behaviour may leads to unrealistic energy estimates [11,13]. Therefore, energy and buildings experts are in need for tools that enable them to model more accurately the influence of occupants on the whole-building performance. Such models can thereby be used as complementary tools for existing industrial ones in order to provide more accurate estimates of residential energy consumption and accompany inhabitants towards the reduction of the energy consumption. Consequently, some design and technical solutions may be better adapted and energy performance contracts (guarantees) may be better adjusted.