Now showing 1 - 5 of 5
- PublicationInvestigation of demand response strategies in a mixed use buildingThis paper investigates demand response measures, using an EnergyPlus simulation model, developed specifically for demand response analysis, in a mixed-used commercial building. The effectiveness of various building pre-conditioning strategies, which were considered for different durations, immediacy and activation time were assessed using the simulation model. Assessment was carried out for a representative summer day and the contribution of the building capacitance as a mechanism for shifting the building electric power demand was evaluated, recording a maximum load reduction of 6.6% of the baseload.
- PublicationEvaluation of Machine Learning Algorithms for Demand Response Potential ForecastingThis paper focuses on the ability of machine learning algorithms to capture the demand response (DR) potential when forecasting the electrical demand of a commercial building. An actual sports-entertainment centre is utilised as a testbed, simulated with EnergyPlus, and the strategy followed during the DR event is the modification of the chiller water temperature of the cooling system. An artificial neural network (ANN) and a support vector machine (SVM) predictive model, are utilised to predict the DR potential of the building, due to the significant amount of execution time of the EnergyPlus model. The data-driven models are trained and tested based on synthetic databases. Results demonstrate that both ANN and SVM models can accurately predict the building electrical power demand for the scenarios without or with daily DR events, whereas both predictive models are not accurate in forecasting the electrical demand during the rebound effect.
- PublicationImplementation of demand response strategies in a multi-purpose commercial building using a whole-building simulation model approachThis paper exploits a whole-building energy simulation approach to develop and evaluate demand response strategies for commercial buildings. The research is motivated by the increasing penetration of renewable energy sources such as wind and solar, which owing to their stochastic nature, means that enhanced integration of demand response measures in buildings is becoming more challenging and complex. Using EnergyPlus, a simulation model of a multi-purpose commercial building was developed and calibrated. Demand response strategies are evaluated for a number of building zones, which utilise different heating, cooling and ventilation equipment. The results show that for events of varying demand response durations, different strategies should be selected for each zone based on their thermal and usage profiles. Overall, a maximum reduction of 14.7% in electrical power demand was recorded when targeting a centralised chiller load, with smaller reductions for other decentralised building loads.
728Scopus© Citations 39
- PublicationModelling of a Multi-purpose Commercial Building for Demand Response AnalysisThis paper examines the implementation of demand response measures, using an EnergyPlus simulation model, in a multipurpose commercial building. The simulation model, which has been developed specifically for demand response analysis, is used to assess the effectiveness of different demand response strategies, which were considered for the building. The strategies were examined for a representative zone within the building and evaluate the contribution of the building capacitance and HVAC equipment operation, as mechanisms for shifting the building electrical demand. Associated zone temperature responses and load shifts are also quantified.
- PublicationCalibration of a commercial building energy simulation model for demand response analysisThis paper describes the calibration process of an EnergyPlus simulation model, for a multi-purpose commercial building, which has been developed specifically for demand response analysis. Power, gas and air temperature data are collected in fifteen minute intervals as part of the calibration process. Real occupancy data are implemented as well. The results indicate a mean bias error of -1.6% for the annual electricity consumption. Calibration under the scope of demand response at zone and equipment level is also described.