1 - 5 of 160
- PublicationA Study on the Trade-off between Energy Forecasting Accuracy and Computational Complexity in Lumped Parameter Building Energy Models(IBPSA, 2018-05-09)The development of urban scale cost-optimal retrofit decision making requires the development of simplified building energy models which provide satisfactory energy prediction accuracy while remaining tractable when implemented at scale. Lumped parameter building energy models are computationally efficient representations of building thermal performance. The current paper introduces a user-led iterative model reduction methodology which identifies potential trade-offs between model complexity (thus computational requirements) and energy estimation accuracy. Model complexity is progressively reduced using an energy performance criterion prior to model trimming. The methodology is applied to a building energy model of a mixed-use building, which is developed in the EnergyPlus Building Energy Model Simulation (BEMS) environment. The energy performance of the building is evaluated using a linear energy minimisation problem. The proposed methodology shows a potential reduction by half of the model complexity is possible, while retaining annual energy estimation errors below 10% for the target building.
- PublicationA study of operation strategy of small scale heat storage devices in residential distribution feeders(IEEE, 2017-09-29)Passive operation of thermal energy storage devices is a well established concept in Europe; this paper looks at active operation of thermal storage devices and their role in providing demand response from residential distribution feeders. It co-simulates the power system and the thermal performance of buildings to investigate the effect of operation strategy of thermal energy storage devices on the network and thermal comfort of households. A realistic residential feeder is used to demonstrate the applicability of the presented methodology. It is shown that the operation strategy of the thermal storage devices can affect the realizable reserve from these devices, house temperature and network variables such as losses and voltage. The realizable demand response found by the presented methodology can be used for market operation to avoid underestimation and overestimation of the demand response.
418Scopus© Citations 6
- PublicationModelling Household Occupancy Profiles using Data Mining Clustering Techniques on Time Use Data(2017-08-09)A strong correlation exists between occupant behaviour and energy demand in residential buildings. The choice of the most suitable occupancy model to be integrated in high temporal resolution energy demand simulations is heavily in uenced by the purpose of the building energy demand model and it is a tradeoff between complexity and accuracy. The current paper introduces a new occupancy model that produces multi-day occupancy profiles and can be adaptable to various occupancy scenarios (e.g., at home all day, mostly absent) and scalable to different population sizes. The methodology exploits data mining clustering techniques with Time Use Survey (TUS) data to produce realistic building occupancy patterns. The overall methodology can be subdivided into two steps: 1. Identification and grouping of households with similar daily occupancy profiles, using data mining clustering techniques; 2. Creation of probabilistic occupancy profiles using 'inverse function method'. The data from the model can be used as input to residential dwelling energy models that use occupancy time-series as inputs.
Scopus© Citations 5 332
- PublicationModelling residential building stock heating load demand - Comparison of occupancy models at large scale(2017-06-28)In the residential housing sector, a strong correlation exists between occupant behaviour and space heating energy use. In particular, the occupancy scenario (e.g., daytime absence, morning presence, etc.) has a significant influence on residential heating load profiles, as well as on cumulative heating energy consumption. In the literature, many occupancy models have been utilised to predict occupancy profiles of individual dwellings as part of larger residential building stocks. The choice of the most suitable occupancy model is a trade-off between complexity, accuracy and computational effort, as well as model integration at large scale. The current paper analyzes the combined influence of different occupancy assumptions and different occupancy models on housing heating loads for a UK building stock sample. The building stock heating loads are estimated using a dynamic thermal model based on an equivalent Resistance-Capacitance electric circuit. It is assumed that the heating periods are coincident with the actively occupied periods. The actively occupied periods are first determined using two existing consolidated occupancy models, and then by using newly developed probabilistic occupancy models. All the models are characterised by a different grade of complexity and accuracy. Comparing the results of all the presented methodologies, the advantages of the new probabilistic approaches are analyzed.
- PublicationSub-hour Unit Commitment MILP Model with Benchmark Problem Instances(Springer, 2017-07-07)Power systems are operated to deliver electricity at minimum cost while adhering to operational and technical constraints. The introduction of smart grid technologies and renewable energy sources offers new challenges and opportunities for the efficient and reliable management of the grid. In this paper we focus on a Mixed Integer Programming sub-hour Unit Commitment model. We present analysis of computational results from a large set of problem instances based on the Irish system and show that problem instances with higher variability in net demand (after the integration of renewables) are more challenging to solve.
364Scopus© Citations 1