Application of a probabilistic LHS-PAWN approach to assess building cooling energy demand uncertainties

A deterministic approach to building energy simulation risks the omission of real-world uncertainties leading to prediction errors.This paper highlights limitations of this approach by contrasting it with a probabilistic uncertainty/sensitivity simulation approach.Latin hypercube sampling(LHS)generates 15000 unique model configurations to assess the effects of weather,physical and operational uncertainties on the annual and peak cooling energy demands for a residential building which situated in a hot and dry climatic region.Probabilistic simulations predicted 0.22–2.17 and 0.45–1.62 times variation in annual and peak cooling energy demands,respectively,compared to deterministic simulation.A novel density-based global sensitivity analysis(SA),i.e.,PAWN,is adopted to identify dominant input uncertainties.Unlike traditional SA methods,PAWN allows simultaneous treatment of continuous and categorical inputs from a generic input-output sample.PAWN is favourable when computational resources are limited and model outputs are skewed or multi-modal.For annual and peak cooling demands,the effects of weather and operational parameters associated with airconditioner and window operation are much stronger than these of other parameters considered.Consequently,these parameters warrant greater attention during modelling and simulation stages.Bootstrapping and convergence analysis also confirm the validity of these results.

Technologies and Practical Implementations of Air-conditioner Based Demand Response

Nowadays,the most notable uncertainty for an electricity utility lies in the electrical demand of end-users.Demand response(DR)has acquired considerable attention due to uncertain generation outputs from intermittent renewable energy sources and advancements of smart grid technologies.The percentage of the air-conditioner(AC)load over the total load demand in a building is usually very high.Therefore,controlling the power demand of ACs is one of significant measures for implementing DR.In this paper,the increasing development of ACs,and their impacts on power demand are firstly introduced,with an overview of possible DR programs.Then,a comprehensive review and discussion on control techniques and DR programs for ACs to manage electricity utilization in residential and commercial energy sectors are carried out.Next,comparative analysis among various programs and projects utilized in different countries for optimizing electricity consumption by ACs is presented.Finally,the conclusions along with future recommendations and challenges for optimal employment of ACs are presented in the perspective of power systems.

Impacts of technology-guided occupant behavior on air-conditioning system control and building energy use

Occupant behavior is an important factor affecting building energy consumption.Many studies have been conducted recently to model occupant behavior and analyze its impact on building energy use.However,to achieve a reduction of energy consumption in buildings,the coordination between occupant behavior and energy-efficient technologies are essential to be considered simultaneously rather than separately considering the development of technologies and the analysis of occupant behavior.It is important to utilize energy-efficient technologies to guide the occupants to avoid unnecessary energy uses.This study,therefore,proposes a new concept,“technology-guided occupant behavior”to coordinate occupant behavior with energy-efficient technologies for building energy controls.The occupants are involved into the control loop of central air-conditioning systems by actively responding to their cooling needs.On-site tests are conducted in a Hong Kong campus building to analyze the performance of“technology-guided occupant behavior”on building energy use.According to the measured data,the occupant behavior guided by the technology could achieve“cooling on demand”principle and hence reduce the energy consumption of central air-conditioning system in the test building about 23.5%,which accounts for about 7.8%of total building electricity use.

The energy performance and passive survivability of high thermal insulation buildings in future climate scenarios

Given that the passive performance simulation of buildings based on typical meteorological year data and specific design schemes makes it challenging to respond to climate change and refine design requirements on time,this article established a passive performance prediction model for future buildings considering multi-dimensional variables including climate change,building design,and operational characteristics.For high thermal insulation buildings under future climates,the mild climate zone is more sensitive than the others,cooling energy demand is more sensitive than heating demand,apartments are more sensitive than office buildings,and passive survivability is more sensitive than energy performance;for buildings of the same type located in the same climate zone,thermal design solutions determine the increase rate of cooling demand.The potential benefits of climate warming on heating demand reduction are almost zero,but the cooling demand increases significantly,with apartments and office buildings increasing up to 22.1% and 5.0%,respectively.Buildings generally overheat in the future,and the increase rate of the mild zone far exceeds other zones with duration and severity being 3004.8% and 877.7%for apartments,and 884.3% and 288.9%for office buildings,respectively.