Now showing 1 - 10 of 10
  • Publication
    Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
    Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.
      66
  • Publication
    Feature Assessment in Data-Driven Models for Unlocking Building Energy Flexibility
    Data-driven approaches are playing an increased role in building automation. This can, in part, be attributed to building operation and energy management system data becoming more readily accessible. A particular application is models to allow predictive control harnessing building energy flexibility, which is of interest to different stakeholders including; energy utilities, aggregators and end-users. Given the possibility of thousands of data features, feature selection becomes a critical part of the model development process. This paper considers various filter, wrapper and embedded methods applied in conjunction with three predictors in addressing the problem of constructing a suitable data-driven model to facilitate predictive control and provision of energy flexibility in a large commercial building. The feature selection algorithms are generally shown to significantly reduce model evaluation time and, in some cases, increase model accuracy. A random forest model with embedded feature selection was found to be the optimal solution in terms of model accuracy.
      178
  • Publication
    Quantification and characterization of energy flexibility in the residential building sector
    (International Building Performance Association, 2019-09-04) ; ; ; ; ;
    Demand response can enable residential consumers to take advantage of control signals and/or financial incentives to adjust the use of their resources at strategic times. These resources usually refer to energy consumption, locally distributed electricity generation, and energy storage. The building structural mass has an inherent potential either to modify consumption or to be used as a storage medium. In this paper, the energy flexibility potential of a residential building thermal mass for the winter design day is investigated. Various active demand response strategies are assessed using two flexibility indicators: the storage efficiency and storage capacity. Using simulation, it is shown that the available capacity and efficiency associated with active demand response actions depend on thermostat setpoint modulation, demand response event duration, heating system rated power and current consumption.
      128
  • Publication
    Data-Driven Predictive Control for Commercial Buildings with Multiple Energy Flexibility Sources
    Data-Driven Predictive Control, representing the building as a cyber-physical system, shows promising potential in harnessing energy flexibility for demand side management, where the efforts in developing a physics-based model can be significant. Here, predictive control using random forests is applied in a case study closed-loop simulation of a large office building with multiple energy flexibility sources, thereby testing the suitability of the technique for such buildings. Further, consideration is given to the feature selection and feature engineering process. The results show that the data-driven predictive control, under a dynamic grid signal, is capable of minimising energy consumption or energy cost.
      77
  • Publication
    Towards Robustness of Data-Driven Predictive Control for Building Energy Flexibility Applications
    Identifying physics-based models of complex dynamical systems such as buildings is challenging for applications such as predictive and optimal control for demand side management in the smart grid. Data-driven predictive control using machine learning algorithms show promise as a more scalable solution when considering the greater building stock. The robustness of these algorithms for different climate data, building types, quality and quantity of data, is still not yet well understood. The objective in this study is to investigate model identification and the resultant accuracy for these various contexts using the `separation of variables' technique (DPC-En) and the consequent performance implications of the data-driven controller. The DPC-En controller is tested using a closed-loop simulation testbed of a `large office' archetype building. The results show that the technique is relatively robust to missing data and different climate types and delivers promising results using limited training data without the need for disruptive excitation measures. This work contributes to enabling a greater proportion of the diverse building stock to be utilised for demand side management by harnessing their inherent energy exibility potential.
      59
  • 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.
      316
  • Publication
    Towards Standardising Market-Independent Indicators for Quantifying Energy Flexibility in Buildings
    Buildings are increasingly being seen as a potential source of energy flexibility to the smart grid as a form of demand side management. Indicators are required to quantify the energy flexibility available from buildings, enabling a basis for a contractual framework between the relevant stakeholders such as end users, aggregators and grid operators. In the literature, there is a lack of consensus and standardisation in terms of approaches and indicators for quantifying energy flexibility. In the present paper, current approaches are reviewed and the most recent and relevant market independent indicators are compared through analysis of four different case studies comprising varying building types, climates and control schemes to assess their robustness and applicability. Of the indicators compared, certain indicators are found to be more suitable for use by the end user when considering energy and carbon dioxide emission reductions. Other indicators are more useful for the grid operator. The recommended indicators are found to be robust to different demand response contexts, such as type of energy flexibility, control scheme, climate and building types. They capture the provided flexibility quantity, its shifting efficiency and rebound effect. A final cost index is also recommended given specific market conditions to capture the cost of a building providing energy flexibility.
      143Scopus© Citations 15
  • Publication
    On the assessment and control optimisation of demand response programs in residential buildings
    The ability to control and optimise energy consumption at end-user level is of increasing interest as a means to achieve a balance between supply and demand, particularly when large penetration of distributed renewable energy sources is being considered. Demand Response programs consist of a series of externally-driven control strategies aimed at adapting consumer end-use load to specific grid requirements. In a demand response scenario, a network of connected systems can be exploited to activate balancing strategies, to provide demand flexibility during periods of high stress for the grid. However, the widespread deployment of demand response programs in the building sector still faces significant challenges. Smart technology deployment, the lack of common standardised assessment procedures and metrics, the absence of established regulatory frameworks are among the main obstacles limiting the development of portfolios of competitive flexibility assets. The residential sector is even more affected by these challenges due to a marginal economic case, the issue of long term harmonisation of hardware and software infrastructure and the influence of the end-user behaviour and preferences on energy consumption. The present paper provides a review on the current developments of the Demand Response programs, with specific reference to the residential building sector. Methodologies and procedures for assessing building energy flexibility and Demand Response programs are described with a special focus on numerical models and available control algorithms. Moreover, markets schemes and social aspects - such as technology acceptance and awareness - and their influence on smart control technologies and algorithms are discussed. Current research gaps and challenges are identified and analysed to provide guidance for future research activities.
      186Scopus© Citations 58
  • Publication
    Investment analysis of gas-turbine combined heat and power systems for commercial buildings under different climatic and market scenarios
    The aim of the proposed work is to investigate the technical and economic suitability of a gas turbine combined heat and power system in commercial buildings. These systems are widely recognised as a promising technology to provide significant fuel savings and carbon emissions reduction where they have been widely used in industrial settings due to the relatively constant electrical and thermal loads required for industrial processes. However, their deployment has been relatively stagnant over the last few decades due to challenges such as poor planning and policy measures, energy market changes and regulatory barriers. In this context, a preliminary system design and optimisation procedure has been developed based on a sensitivity analysis of different scenarios of building loads, market and weather conditions. The optimisation is performed considering several technical and environmental parameters (e.g., energy and exergy efficiencies and primary energy saving), as well as economic indexes (e.g., net present value, pay-back period, profitability, etc.). This allows the suitability assessment of the investment for different market price scenarios under different heating degree days demand scenarios. The analysis is carried out using an Italian case study as it exhibits a wide range of heating degree days variability, while subject to a single pricing market. Results show that strong correlations occur between the technical and economic performance indices and the weather conditions for all considered configurations. The methodology and conclusions, if coupled with the possibility of applying clustering techniques to determine common patterns of energy consumptions in building blocks, represent a powerful toolset to carry out preliminary techno-economic assessment of a combined heat and power system.
      185Scopus© Citations 16
  • Publication
    Mapping the energy flexibility potential of single buildings equipped with optimally-controlled heat pump, gas boilers and thermal storage
    The present paper assesses the capability of a cost-optimal control strategy to activate demand response actions in a building equipped with an air-source heat pump coupled with a water thermal storage system. Commencing with a reference scenario where no demand response actions are considered, the electricity consumption pattern and the operational cost are evaluated. Several demand response scenarios are next considered by adapting consumption patterns by reduction of baseline heat pump power consumption. The difference between the operational cost evaluated under a specific demand response program and the benchmark cost are used to assess the marginal cost that should be considered to provide incentives to promote user participation in demand response programs. The results illustrate the effectiveness of thermal energy storage for reducing the total system operational cost and its seasonal primary energy consumption, both with and without demand response actions. The application of the proposed methodology over the whole heating season, allows performance maps to be created that can be used either by the grid-operator or end-user to identify the best demand response action to be implemented on any particular day. These maps represent useful decision tools to assess and optimise the flexibility potential while meeting end-user needs.
      200Scopus© Citations 39