Now showing 1 - 5 of 5
  • Publication
    A Study on the Trade-off between Energy Forecasting Accuracy and Computational Complexity in Lumped Parameter Building Energy Models
    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.
  • Publication
    Lumped Parameter Building Model Calibration using Particle Swarm Optimization
    This paper presents a methodology for the automated calibration of deterministic lumped parameter models in building energy simulation using optimization methods. A heterogeneous model topology is proposed to represent a residential building archetype developed in the EnergyPlus simulation environment. The archetype model has previously been used to characterize the domestic building stock in Ireland. The automated calibration problem is solved as a least squares error problem solved using a local optimization method (Sequential Quadratic Programming) and two heuristics methods (Particle Swarm Optimization and Genetic Algorithm). It is shown that Particle Swarm Optimization provides the best performance for this particular problem and provides an inherent robustness under model uncertainty.
  • Publication
    An integrated Building-to-Grid model for evaluation of energy arbitrage value of Thermal Storage
    Thermal Electric Storage (TES) has emerged as a promising technology for enhancing the flexibility of the built environment to participate in active Demand Side Management (DSM). These devices allow the decoupling of intra-day scheduling of electric power demand from the time of thermal energy end-use. Therefore, if enabled with communication with the grid, these devices can facilitate load shifting and energy arbitrage. This study evaluates the energy arbitrage value of smart TES devices in residential buildings across Ireland. A Building-to-Grid (B2G) model has been developed which integrates the buildings thermal dynamics and end-use constraints with the power systems economic dispatch model. The thermal behavior of the houses and the TES space heater and hot water tank is modeled through linear state space models for three different mid-flat archetypes. The optimization results show the load shifting and arbitrage potential of TES and its impacts on wind curtailment considering various penetration levels of these devices.
  • Publication
    Ensemble Calibration of Lumped Parameter Retrofit Building Models using Particle Swarm Optimization
    Simulation-based building retrofit analysis tools and electricity grid expansion planning tools are not readily compatible. Their integration is required for the combined study of building retrofit measures and electrified heating technologies using low-carbon electricity generation. The direct coupling of these modelling frameworks requires the explicit mathematical representation of Energy Conservation Measures (ECMs) in building-to-grid energy system models. The current paper introduces an automated calibration methodology which describes retrofitted buildings as parametric functions of ECMs. The buildings are represented using a lumped parameter modelling framework. A baseline model, representative of the building prior to retrofit, and the retrofit functions are calibrated using Particle Swarm Optimization. Synthetic temperature and heating load time-series data were generated using an EnergyPlus semi-detached house archetype model. The model is representative of this residential building category in Ireland. It is shown that the proposed methodology calibrates retrofitted building models to an acceptable level of accuracy (MAE below 0.5 °C). The methodologies introduced in the current paper are capable of generating lumped parameter building models with similar dynamics for different ECMs for any archetype building energy model. The identified building retrofit models have the potential to be integrated with electricity grid models in a computationally-efficient manner.
      410Scopus© Citations 16
  • Publication
    A study of operation strategy of small scale heat storage devices in residential distribution feeders
    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.
      328Scopus© Citations 6