Identification of flexible buildings with bending deformation and the unmeasured earthquake ground motion

It is important to explore efficient algorithms for the identification of both structural parameters and unmeasured earthquake ground motion.Recently,the authors proposed an algorithm for the identification of shear-type buildings and unknown earthquake excitation.In this paper,it is extended to the investigation of the identification of flexible buildings with bending deformation and the unmeasured earthquake ground motion.In the absolute co-ordinate system,the unmeasured ground motion can be treated as an unknown translational force and a bending moment at the 1st floor level of a flexible building.Structural unknown parameters above the 1st story of the building can be identified by the extended Kalman estimator and the 1st story stiffness and the unmeasured ground motion are subsequently estimated based on the least-squares estimation.The proposed algorithm is further extended to the identification of tall bending-type buildings based on substructure approach.Inter-connection effect between sub-buildings is treated as‘additional unknown inputs’to sub-buildings,which are estimated by the extended Kalman estimator without the measurements of rotational responses.Numerical examples demonstrate the identification of a multi-story,tall bending-type building and its unmeasured earthquake ground motions using only partial measurements of structural absolute responses.

In search of optimal building behavior models for model predictive control in the context of flexibility

Model predictive control(MPC)is an advanced control technique.It has been deployed to harness the energy flexibility of a building.MPC requires a dynamic model of the building to achieve such an objective.However,developing a suitable predictive model is the main challenge in MPC implementation forflexibility activation.This studyfocuses on the application of key performance indicators(KPls)to evaluate the suitability of MPC models via feature selection.To this end,multiple models were developed for two houses.A feature selection method was developed to select an appropriate feature space to train the models.These predictive models were then quantified based on one-step ahead prediction error(OSPE),a standard KPI used in multiple studies,and a less-often KPl:multi-step ahead prediction error(MSPE).An MPC workflow was designed where different models can serve as the predictive model.Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation.Results revealed that up to 57% of the flexibility potential and 48% of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.

Exploring Potential of Energy Flexibility in Buildings for Energy System Services

Buildings have both high as well as flexible energy demands and play an important role in the energy internet solution.The buildings’energy flexibility(BEF)is a widely recognized concept;however,how to unlock its potential is a relatively new research topic.In this paper,the authors provide an overview of the latest research related to BEF.An introduction to BEF is provided,methods developed for identifying and characterizing BEF are presented,and several key influencing factors are identified.The overview also covers various aggregation methods to scale up BEF impacts and service-oriented solutions for enabling BEF applications in different energy sectors.This work lays the groundwork for designing and developing seamless integration strategies for BEF use in both present and future energy systems.

Development of a Soft Actor Critic deep reinforcement learning approach for harnessing energy flexibility in a Large Office building

This research is concerned with the novel application and investigation of‘Soft Actor Critic’based deep reinforcement learning to control the cooling setpoint(and hence cooling loads)of a large commercial building to harness energy flexibility.The research is motivated by the challenge associated with the development and application of conventional model-based control approaches at scale to the wider building stock.Soft Actor Critic is a model-free deep reinforcement learning technique that is able to handle continuous action spaces and which has seen limited application to real-life or high-fidelity simulation implementations in the context of automated and intelligent control of building energy systems.Such control techniques are seen as one possible solution to supporting the operation of a smart,sustainable and future electrical grid.This research tests the suitability of the technique through training and deployment of the agent on an EnergyPlus based environment of the office building.The agent was found to learn an optimal control policy that was able to minimise energy costs by 9.7%compared to the default rule-based control scheme and was able to improve or maintain thermal comfort limits over a test period of one week.The algorithm was shown to be robust to the different hyperparameters and this optimal control policy was learnt through the use of a minimal state space consisting of readily available variables.The robustness of the algorithm was tested through investigation of the speed of learning and ability to deploy to different seasons and climates.It was found that the agent requires minimal training sample points and outperforms the baseline after three months of operation and also without disruption to thermal comfort during this period.The agent is transferable to other climates and seasons although further retraining or hyperparameter tuning is recommended.

Worldwide carbon neutrality transition?Energy efficiency,renewable,carbon trading and advanced energy policies

Climate change and energy shortage crisis promptly necessitate achievement of sustainable development goals.However,there is no straightforward pathways for low-carbon transformation on building sectors,and energy/carbon trading and reverse promotion on decarbonization strategies are not clear.In this study,a literature enumeration method with dialectical analysis was adopted for state-of-the-art literature review and comparison.Low-carbon transformation pathways in buildings were holistically reviewed,with a series of integrated techniques,such as energy saving,clean energy supply,flexible demand response for high self-consumption,and even smart electric vehicle(EV)integration.Afterwards,energy/carbon flows and trading in building-related systems were provided,such as peer-to-peer energy trading,building and thermal/power grids,building and energyintegrated EVs,and carbon trading in buildings.Last but not the least,worldwide decarbonization roadmaps across regions and countries are analysed,to identify the most critical aspects and immediate actions on decarbonization.Results indicate that tradeoff strategies are required to compromise the confliction between insufficient feed-in tariff(FiT)incentives(low renewable penetration in the market)and great economic pressures(high investment in renewable systems).Low-carbon building pathway is further enhanced with first priority given to passive/active energy-saving strategies,onsite clean energy supply and then flexible demand response.Energy/carbon trading will significantly affect renewable energy utilization,and acceptance from end-users to actively install renewable systems or participate in EV interactions.Worldwide decarbonization pathways mainly focus on industries,transportation,buildings,renewable sources,carbon sink and carbon capture,utilization and storage(CCUS).This study can contribute to technical roadmaps and strategies on carbon neutrality transition in both academia and industry,together with advanced policies in grid feed-in tariff,energy/carbon trading and business models worldwide.

Model-based investigation on building thermal mass utilization and flexibility enhancement of air conditioning loads

Building air conditioning systems(ACs)can contribute to the stable operation of power grids by participating in peak load shaving programs,but the participants need a fast and accurate zone temperature prediction model,e.g.,the detailed room thermal-resistance(RC)model,to improve peak shaving effect and avoid obvious thermal discomfort.However,when applying the detailed room RC model to multi-zone buildings,conventional studies mostly consider the heat transfer among neighboring rooms,which contributes little to the prediction accuracy improvement,but leads to complicated model structure and heavy computation.Thus,a distributed RC model is developed for multi-zone buildings in this study.Compared to conventional models,the proposed model considers the total heat transfer between the building and the air,and ignores the heat transfer among indoor air in neighboring rooms through internal walls with heavy thermal mass,thereby having comparable temperature prediction accuracy,simpler structure,and stronger robustness.Based on the model,the effectiveness of passive pre-cooling strategies in reducing the air conditioning loads during peak periods is investigated.Results indicate that the thermal insulation performance of opaque building envelope is quite important to the flexibility enhancement of air conditioning loads.With an uninsulated building envelope,passive pre-cooling is useless for the peak load shaving.In comparison,well insulated opaque building envelope enables the building thermal mass to be utilized through passive pre-cooling,which leads to the air conditioning loads during peak periods being further reduced by about 12%.