Abstract
High share of energy consumption in buildings and subsequent increase in greenhouse gas emissions along with stricter legislations have motivated researchers to look for sustainable solutions in order to reduce energy consumption by using alternative renewable energy resources and improving the efficiency in this sector. Today, the smart building and socially resilient city concepts have been introduced where building automation technologies are implemented to manage and control the energy generation/consumption/storage. Building automation and control systems can be roughly classified into traditional and advanced control strategies. Traditional strategies are not a viable choice for more sophisticated features required in smart buildings. The main focus of this paper is to review advanced control strategies and their impact on buildings and technical systems with respect to energy/cost saving. These strategies should be predictive/responsive/adaptive against weather, user, grid and thermal mass. In this context, special attention is paid to model predictive control and adaptive control strategies. Although model predictive control is the most common type used in buildings, it is not well suited for systems consisting of uncertainties and unpredictable data. Thus, adaptive predictive control strategies are being developed to address these shortcomings. Despite great progress in this field, the quantified results of these strategies reported in literature showed a high level of inconsistency. This is due to the application of different control modes, various boundary conditions, hypotheses, fields of application, and type of energy consumption in different studies. Thus, this review assesses the implementations and configurations of advanced control solutions and highlights research gaps in this field that need further investigations.
Highlights
Energy demand in the residential and commercial buildings sector in the European Union (EU) accounts for approximately 40 % of total en ergy consumption (European Commission, 2019a). The outcome of this high share of energy consumption has been a significant increase in greenhouse gas (GHG) emissions from 19 % in 2010 to 39 % (Eurostat, 2018)
In line with EU legislation, the nearly zero energy building and low/zero carbon concepts have been introduced as the basic components of sustainable and socially resilient smart cities and society. nZEB has a very high energy performance with the nearly zero or minimum amount of energy consumption which is mainly sup plied from Renewable Energy Sources (RESs) either on-site or nearby (D’Agostino, Zangheri, Cuniberti, Paci, & Bertoldi, 2016; Kolokotsa, Rovas, Kosmatopoulos, & Kalaitzakis, 2011)
The cloud platform gathers all the data including external environmental data; current external weather con dition and grids’ TOU price, disturbances prediction parameters and data collected from local central control unit (CCU) including internal environmental data; parameters related to indoor thermal comfort, HVAC operation, user’s behaviour and user’s current need
Summary
Energy demand in the residential and commercial buildings sector in the European Union (EU) accounts for approximately 40 % of total en ergy consumption (European Commission, 2019a). A common application of MPC in the building sector comprises the prediction of the dynamic behavior of systems in the future and adjustment of response by the controller leading to energy and cost saving while satisfying thermal comfort (Hazyuk et al, 2014; Serale et al, 2018). Despite many reports on the merit of ACS for energy/cost savings, a deep review of the reported results published in the literature shows that they are highly inconsistent and widely variable These great variabil ities could be attributed to the applied hypotheses comprising different boundary conditions, control system configurations, technical system application and the practical implementation of the control system. (Tanaskovic, Sturzenegger, Smith, & Morari, 2017) reported the adap tive MPC strategy was an efficient solution for handling uncertainties in building climate control while satisfying comfort even during the adaptation phase
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