1,720,967 research outputs found
Design for energy flexibility in smart buildings through solar based and thermal storage systems: Modelling, simulation and control for the system optimization
No full textThe present study investigates the use and implementation of energy efficient measures and strategies for building applications, toward the Nearly Zero Energy Buildings target. Specifically, objective of the study is to implement building integrated photovoltaic thermal devices coupled with a phase change materials heat exchanger acting as an active thermal storage building component, with the aim to add flexibility to the building while still maintaining indoor comfort conditions. To show the potentials of the novel configuration proposed in this paper, a multi-zone grey-box model is developed and validated to capture the thermal dynamics of a building, and a control strategy applied to the whole system is developed for energy management purpose. The whole simulation model, including thermophysical properties of the building-system and the control features, is implemented in a MATLAB environment. To assess the model and application potentials toward the optimal design and operation of the proposed system for energy efficiency and flexibility goals, a suitable case study analysis is conducted. Thus, a sensitivity analysis, using an evolutionary algorithm, is performed by considering economic and energy objective functions which focuses on the reduction of the building energy demand, load variability and economic aspects. In this regard, the optimal design configuration is underlined in a way that the operation of the components can be maximized to provide flexibility to the building: in average working conditions one single layer of PCM can provide around 186.3 Wh/K per unit of temperature and width. A rule-based management strategy is proposed to prove the possibility to shift and shave the energy peaks during high energy request periods, demand response events. Finally, by considering an approximate economic calculation, the simple payback, taking into account only the positive effects on the winter management, is around 13.5 years
Optimizing energy flexibility through electricity price-responsiveness and thermal load management in buildings with convective and radiant heating systems
No full textBuilding energy modeling is essential for designing energy-efficient and flexible buildings that seamlessly integrate with the electrical grid. This study introduces a data-driven, control-oriented methodology using Resistance-Capacitance thermal network models to accurately forecast building thermal loads. It differentiates the impacts of fast and slow dynamics associated with different heating types—radiant and/or convective. A Model Predictive Control (MPC) framework optimizes coordination between the different building thermal dynamics, considering weather forecasts and price signals. The Varennes Library, a Net Zero Energy Institutional Building located in Québec (Canada), serves as a case study for performance assessment.
Validation of the developed model demonstrates its efficacy in enabling MPC to formulate effective control strategies. Findings reveal that high-mass radiant heating is strategically used before indoor setpoint variation or demand response events. Up to 70% of the building thermal load is delivered to the active envelope for off-peak heat storage and on-peak release. Conversely the ventilation heating is prioritized in proximity of the change in setpoint or grid tariff with percentages over 80%. Results show the adoption of weather clusters for generalizing the optimal control setting, highlighting their influence on thermal loads while maintaining robust ventilation and active envelope heating coordination. The comparison between the predictive control strategy and the existing rule-based control shows improvements in indoor temperature and energy flexibility. During the MPC routine, a constant price signal reduces grid stress, achieving Load Factor (LF) values up to 0.72 compared to 0.60 with rule-based control, while demand response, though critical peak pricing, optimally shifts up to 100% of the thermal load during peak price hours
Simulation and control for energy management: an energy management strategy applied to a multi-zone building coupled with solar based and energy storage technologies.
No full textModelling and control for energy flexibility distinguishing fast and slow response in a Net Zero Institutional Building: the Varennes Library
No full textModel predictive control for energy flexibility of a building coupled with advanced solar and energy storage technologies
No full textAutomated model order reduction for building thermal load prediction using smart thermostats data
No full textThis paper presents a methodology to automatically determine the structure of sufficiently accurate grey-box models for model predictive control, energy efficiency and flexibility applications in buildings. The methodology is based on model reduction and system identification techniques, with a path that enhances data pre-processing, a multistage order reduction, and parameter estimation. The model structure is determined with a cascade approach that either neglects, keeps, or aggregates thermal zones by using discrete and continuous frequency domain techniques. Once the optimal structure is identified, the parameters are calibrated with the measured data from smart thermostats, using the model predictive control relevant identification method. The methodology is applied to a monitored house located in Québec, Canada. The developed algorithm identifies adjacent zones, even when the building layout is unknown, by studying indoor temperature fluctuations. The results concerning the model creation suggest that, for this specific building, the aggregation by floor is the most efficient way for creating reduced order thermal models, limiting uncertainty due to thermal zone interaction. This methodology provides control-oriented models that accurately predict response up to 24-h ahead with Root Mean Square Error less than 0.5 °C and acceptable Fitness Function values for the minimum number of selected parameters. Finally, several scenarios demonstrate the insights gained from using grey-box building thermal models for design, control, and retrofitting applications
A novel multi-level predictive management strategy to optimize phase-change energy storage and building-integrated renewable technologies operation under dynamic tariffs
No full textIn the evolving energy sector, where buildings are recognized as dynamic components of energy networks and smart grids, the implementation of new regulations and guidelines is crucial to optimize the interaction between buildings and the grid. The imperative to reduce building energy consumption facilitates the promotion of new technologies that rely on renewable energy generation and innovative materials. As technology has progressed, the multitude of energy vectors involved has made controlling energy systems increasingly challenging. This poses a barrier to the widespread adoption and implementation of cutting-edge technologies. In this framework, this paper explores an energy-efficient solution using an integrated photovoltaic/thermal collector and an active phase-change material storage system. The study optimizes the integration of technologies through a resistance capacitance model, assessing the impact on thermal comfort, energy savings and costs. A novel cascade methodology, combining particle swarm optimization search with model predictive control, is designed to select the optimal mode of operation for the proposed technologies. The economic feasibility of the proposed system is analyzed across different tariff structures, whereas the interaction with the grid is evaluated using energy flexibility key performance indicators. The energy and economic performance, as well as the flexibility of the system are assessed through a proof-of-concept conducted in an office building scenario. The results demonstrate an increase in energy efficiency, with savings ranging from 9% to 28% compared to a suitable baseline scenario, and a significant energy shift from on-peak to off-peak periods, potentially accounting for up to 46% of the total building load. This energy flexibility enables the grid to receive reduced demand during morning hours
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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