Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions

Li, Xiwang; Malkawi, Ali

doi:10.1016/j.energy.2016.07.021

Cited by 93 publications

(65 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studying six small and medium-sized commercial buildings in the cities of Boston, Chicago, and Miami [123] demonstrates energy cost savings of 20%-60% for HVAC operation on five days in August when compared to a pre-cooling night-setback strategy using three-year average weather data. The savings depend on the individual building and its location as well as the relative weight of comfort compared to energy cost.…”

Section: Theoreticalmentioning

confidence: 99%

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Schmidt

Åhlund

2018

Renewable and Sustainable Energy Reviews

107

View full text Add to dashboard Cite

Due to its significant contribution to global energy usage and the associated greenhouse gas emissions, existing building stock's energy efficiency must improve. Predictive building control promises to contribute to that by increasing the efficiency of building operations. Predictive control complements other means to increase performance such as refurbishments as well as modernizations of systems. This survey reviews recent works and contextualizes these with the current state of the art of interrelated topics in data handling, building automation, distributed control, and semantics. The comprehensive overview leads to seven research questions guiding future research directions.

show abstract

Section: Theoreticalmentioning

confidence: 99%

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Schmidt

Åhlund

2018

Renewable and Sustainable Energy Reviews

107

View full text Add to dashboard Cite

show abstract

“…Thermal mass of a building is natural thermal storage media. If controlled or managed appropriately, the use of heavyweight construction materials has various advantages owning to their high TES capacities including (1) dampen the wide range temperature fluctuation from the outdoor [88]; (2) reduce the peak heating or cooling power demands [89]; (3) maintain the indoor thermal comfort [102]; (4) reduce lifecycle CO 2 emissions [91]; and (5) resist to structural damage by severe storms [92].…”

Section: Thermal Massmentioning

confidence: 99%

“…A model-based demand-limiting control of building thermal mass was developed by Lee and Braun [101]. Recently, Li and Malkawi [102] developed a multi-objective optimization based model predictive control framework that takes both energy cost and thermal comfort into consideration simultaneously. For residential buildings, Le Dréau and Heiselberg [103] found that the thermal mass storage potential depends largely on the insulation level thus the control strategy should be designed differently to make use of the flexibility potential without violating the comfort.…”

Section: Thermal Massmentioning

confidence: 99%

Energy Storage by Sensible Heat for Buildings

Fan¹

2018

Handbook of Energy Systems in Green Buildings

View full text Add to dashboard Cite

This chapter presents a state-of-the-art review on the available thermal energy storage (TES) technologies by sensible heat for building applications. After a brief introduction, the basic principles and the required features for desired sensible heat storage are summarized. Then, material candidates and recent advances on sensible heat or cold storage adapted for building application are discussed, each with its own characteristics, advantages, and limitations. A large section of the chapter is devoted to the sensible TES technologies for buildings, both for short-term (daily) and for long-term (seasonal) storage. Each technology is described in detail including different aspects: basic principle, development status, performance and costs, potential and barriers, today's R&D activity focus, etc. Comparisons on the advantages and limitations between different TES technologies are also made. Finally, conclusions and future directions are summarized.

show abstract

“…In [16], an equivalent resistance-capacitance network was used to build the thermal model of commercial buildings and the starting point was obtained by a quasi-steady-state approach to estimate hourly electricity demand. Li et al [17] exploited the model predictive control (MPC) for building thermal mass control. The authors used a TOU-based program for reducing energy consumption and cost.…”

Section: Introductionmentioning

confidence: 99%

A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

Han

Wang

et al. 2018

Energies

View full text Add to dashboard Cite

Demand response (DR) has become an effective and critical method for obtaining better savings on energy consumption and cost. Buildings are the potential demand response resource since they contribute nearly 50% of the electricity usage. Currently, more DR applications for buildings were rule-based or utilized a simplified physical model. These methods may not fully embody the interaction among various features in the building. Based on the tree model, this paper presents a novel model based control with a random forest (MBCRF) learning algorithm for the demand response of commercial buildings. The baseline load of demand response and optimal control strategies are solved to respond to the DR request signals during peak load periods. Energy cost saving of the building is achieved and occupant’s thermal comfort is guaranteed simultaneously. A linguistic if-then rules-based optimal feature selection framework is also utilized to redefine the training and test set. Numerical testing results of the Pennsylvania-Jersey-Maryland (PJM) electricity market and Research and Support Facility (RSF) building show that the load forecasting error is as low as 1.28%. The peak load reduction is up to 40 kW, which achieves a 15% curtailment and outperforms rule-based DR by 5.6%.

show abstract

Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions

Cited by 93 publications

References 30 publications

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency

Energy Storage by Sensible Heat for Buildings

A MBCRF Algorithm Based on Ensemble Learning for Building Demand Response Considering the Thermal Comfort

Contact Info

Product

Resources

About