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This research paper aims at proposing a framework based on semantic integration in Big Data for saving energy in smart cities. The presented approach highlights the potential opportunities offered by Big Data and ontologies to reduce energy consumption in smart cities.

This study provides an overview of semantics in Big Data and reviews various works that investigate energy saving in smart homes and cities. To reach this end, we propose an efficient architecture based on the cooperation between ontology, Big Data, and Multi-Agent Systems. Furthermore, the proposed approach shows the strength of these technologies to reduce energy consumption in smart cities.

Through this research, we seek to clarify and explain both the role of Multi-Agent System and ontology paradigms to improve systems interoperability. Indeed, it is useful to develop the proposed architecture based on Big Data. This study highlights the opportunities offered when they are combined together to provide a reliable system for saving energy in smart cities.

The significant advancement of contemporary applications (smart cities, social networks, health care, IoT, etc.) requires a vast emergence of Big Data and semantics technologies in these fields. The obtained results provide an improved vision of energy-saving and environmental protection while keeping the inhabitants’ comfort.

This work is an efficient contribution that provides more comprehensive solutions to ontology integration in the Big Data environment. We have used all available data to reduce energy consumption, promote the change of inhabitant’s behavior, offer the required comfort, and implement an effective long-term energy policy in a smart and sustainable environment.

Big Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.

The 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).

Evaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.

This study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.

This research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.

The aim of this paper is to discuss a selection of policy strategies, regional initiatives and market approaches to uncover the realities of twenty-first-century building energy performance. A position that market-based approaches, human influence and policy interventions are part of an ecosystem of building energy performance is presented.

An exploratory search of secondary sources spanning the last three decades was conducted. Both peer-reviewed and grey literature were included to capture a broader understanding of the discourse in literature. Research questions guided the literature search, and a data extraction tool was designed to categorise the literature. The primary limitation of this study is that only a few applications could be discussed in a condensed format.

Several challenges about the current status quo of building energy performance were identified and summarised as follows. (1) Inconsistencies in measurement and verification protocols, (2) Impacts of market approaches, (3) National policy priorities that are at variance with regional targets and (4) Ambiguous reporting on environmental impacts of energy efficiency (EE) technologies.

The practical implications of the findings in this paper for practice and research are that as part of the building energy performance ecosystem, national responses through government interventions must become adaptive to keep up with the fast-paced energy sector and social trends. Simultaneously, before market-based approaches overcome the messiness of socio-economic dynamics, institutional conditions and cultural nuances, they ought to transparently address environmental impacts and the infringement of several SDGs before they can become viable solutions to building energy performance.

This paper presents building energy performance as an ecosystem comprising human influence, market-based approaches and policy interventions which form interdependent parts of the whole. However, evidence in the literature shows that these aspects are usually investigated separately. By presenting them as an ecosystem, this paper contributes to the discourse by advocating the need to re-align building energy performance to socio-economic-political dynamics and contextually viable solutions.