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To reduce the electricity consumption in our homes, a first step is to make the user aware of it. Reading a meter once in a month is not enough, instead, it requires real-time meter reading. Smart electricity meter (SEM) is capable of providing a quick and exact meter reading in real-time at regular time intervals. SEM generates a considerable amount of household electricity consumption data in an incremental manner. However, such data has embedded load patterns and hidden information to extract and learn consumer behavior. The extracted load patterns from data clustering should be updated because consumer behaviors may be changed over time. The purpose of this study is to update the new clustering results based on the old data rather than to re-cluster all of the data from scratch.

This paper proposes an incremental clustering with nearness factor (ICNF) algorithm to update load patterns without overall daily load curve clustering.

Extensive experiments are implemented on real-world SEM data of Irish Social Science Data Archive (Ireland) data set. The results are evaluated by both accuracy measures and clustering validity indices, which indicate that proposed method is useful for using the enormous amount of smart meter data to understand customers’ electricity consumption behaviors.

ICNF can provide an efficient response for electricity consumption patterns analysis to end consumers via SEMs.

In this study, the authors propose a confirmatory factor analysis (CFA) to create a tenable measurement model and identify the factors that have the potential to enhance awareness of pro-environmental measures. The successful implementation of demand response (DR) programs and their required infrastructure is significant for moving towards green energy communities and a better environment for living. Not only can renewable energy capacities contribute to this desideratum, but also electricity consumers who, until the last decade, have played a passive role.

To answer these questions, a complex data set of 243 post-trial questions created by the Irish CER are analyzed using first-order and hierarchical CFA models with several SAS procedures (PROC CALIS, MIANALYZE). The questionnaire was launched to over 3,000 electricity consumers from Ireland that were participants to a trial program after the installation of smart metering systems and implementation of DR programs.

The effect of five latent factors – positive attitude, negative attitude, perceived impact of own actions, price- and incentive-DR programs – is measured. With a bi-factor CFA measurement model, the authors assess that they significantly influence the electricity consumers' awareness.

However, these findings have to be backed up by relevant information and simulations showing consumers benefits in exchange to their efforts. They have research implications on the design of the business models and DR programs pointing out the importance of benefits and fairness of value sharing mechanisms within energy communities.

Thus, the electricity consumers may change their consumption behavior as they positively perceive the implementation of DR programs.

This paper fulfills an identified need to study post-trial questionnaire and reveal latent factors that make electricity consumer change their behavior.

Many power producers are looking for ways to develop smarter energy capabilities to tackle challenges in the sophisticated, non-linear dynamic processes due to the complicated operating conditions. One prominent strategy is to deploy advanced intelligence systems and analytics to monitor key performance indicators, capture insights about the behavior of the electricity generation processes, and identify factors affecting combustion efficiency. Thus, the purpose of this paper is to outline a way to incorporate a business intelligence framework into existing coal-fired power plant data to transform the data into insights and deliver analytical solutions to power producers.

The proposed ten-step business intelligence framework combines the architectures of database management, business analytics, business performance management, and data visualization to manage existing enterprise data in a coal-fired power plant.

The results of this study provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and consequently explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

Once the framework is integrated into the power generation process, it is important to ensure that the top management and the data analysts at the plants have the same perceptions of the benefits of big data and analytics in the long run and continue to provide support and awareness of the use of business intelligence technology and infrastructure in operational decision making.

The key finding of this study helps the power plant prioritize the important factors associated with the emission of NO_x; closer attention to those factors can be promptly initiated in order to improve the performance of the plant.

The use of big data is not just about implementing new technologies to store and manage bigger databases but rather about extracting value and creating insights from large volumes of data. The challenge is to strategically and operationally reconsider the entire process not only to prepare, integrate, and manage big data but also to make proper decisions as to which data to select for the analysis and how to apply analytical techniques to create value from the data that is in line with the strategic direction of the enterprise. This study seeks to fill this gap by outlining how to implement the proposed business intelligence framework to provide plant-wide signals of any unusual operational and coal-quality factors that impact the level of NO_x and to explain and predict the leading causes of variation in the emission of NO_x in the combustion process.

The purpose of this study is to explore the latest approaches in integrating artificial intelligence and analytics (AIA) in energy smart grid projects. Empirical results are synthesized to highlight their relevance from a technology and project management standpoint, identifying several lessons learned that can be used for planning highly integrated and automated smart grid projects.

A systematic literature review leads to selecting 108 research articles dealing with smart grids and AIA applications. Keywords are based on the following research questions: What is the growth trend in Smart Grid projects using intelligent systems and data analytics? What business value is offered when AI-based methods are applied? How do applications of intelligent systems combine with data analytics? What lessons can be learned for Smart Grid and AIA projects?

The 108 selected articles are classified according to the following four research issues in smart grids project management: AIA integrated applications; AI-focused technologies; analytics-focused technologies; architecture and design methods. A broad set of smart grid functionality is reviewed, seeking to find commonality among several applications, including as follows: dynamic energy management; automation of extract, transform and load for Supervisory Control And Data Acquisition (SCADA) systems data; multi-level representations of data; the relationship between the standard three-phase transforms and modern data analytics; real-time or short-time voltage stability assessment; smart city architecture; home energy management system; building energy consumption; automated fault and disturbance analysis; and power quality control.

Given the diversity of issues reviewed, a more capability-focused research agenda is needed to further synthesize empirical findings for AI-based smart grids. Research may converge toward more focus on business rules systems, that may best support smart grid design, proof development, governance and effectiveness. These AIA technologies must be further integrated with smart grid project management methodologies and enable a greater diversity of renewable and non-renewable production sources.

This paper aims to focus on data analytic tools and integrated data analyzing approaches used on smart energy meters (SEMs). Furthermore, while observing the diverse techniques and frameworks of data analysis of SEM, the authors propose a novel framework for SEM by using gamification approach for enhancing the involvement of consumers to conserve energy and improve efficiency.

A few research strategies have been accounted for analyzing the raw data, yet at the same time, a considerable measure of work should be done in making these commercially reasonable. Data analytic tools and integrated data analyzing approaches are used on SEMs. Furthermore, while observing the diverse techniques and frameworks of data analysis of SEM, the authors propose a novel framework for SEM by using gamification approach for enhancing the involvement of consumers to conserve energy and improve efficiency. Advantages of SEM’s are additionally discussed for inspiring consumers, utilities and their respective partners.

Consumers, utilities and researchers can also take benefit of the recommended framework by planning their routine activities and enjoying rewards offered by gamification approach. Through gamification, consumers’ commitment enhances, and it changes their less manageable conduct on an intentional premise. The practical implementation of such approaches showed the improved energy efficiency as a consequence.

Smart grid is an integration between traditional electricity grid and communication systems and networks. Providing reliable services and functions is a critical challenge for the success and diffusion of smart grids that needs to be addressed. The purpose of this study is to determine the critical criteria that affect smart grid reliability from the perspective of users and investigate the role big data plays in smart grid reliability.

This study presents a model to investigate and identify criteria that influence smart grid reliability from the perspective of users. The model consists of 12 sub-criteria covering big data management, communication system and system characteristics aspects. Multi-criteria decision-making approach is applied to analyze data and prioritize the criteria using the fuzzy analytic hierarchy process based on the triangular fuzzy numbers. Data was collected from 16 experts in the fields of smart grid and Internet of things.

The results show that the “Big Data Management” criterion has a significant impact on smart grid reliability followed by the “System Characteristics” criterion. The “Data Analytics” and the “Data Visualization” were ranked as the most influential sub-criteria on smart grid reliability. Moreover, sensitivity analysis has been applied to investigate the stability and robustness of results. The findings of this paper provide useful implications for academicians, engineers, policymakers and many other smart grid stakeholders.

The users are not expected to actively participate in smart grid and its services without understanding their perceptions on smart grid reliability. Very few works have studied smart grid reliability from the perspective of users. This study attempts to fill this considerable gap in literature by proposing a fuzzy model to prioritize smart grid reliability criteria.

Introduction: With the proliferation and amalgamation of technology and the emergence of artificial intelligence and the internet of things, society is now facing a rapid explosion in big data. However, this explosion needs to be handled with care. Ethically managing big data is of great importance. If left unmanageable, it can create a bubble of data waste and not help society achieve human well-being, sustainable economic growth, and development.

Purpose: This chapter aims to understand different perspectives of big data. One philosophy of big data is defined by its volume and versatility, with an annual increase of 40% per annum. The other view represents its capability in dealing with multiple global issues fuelling innovation. This chapter will also offer insight into various ways to deal with societal problems, provide solutions to achieve economic growth, and aid vulnerable sections via sustainable development goals (SDGs).

Methodology: This chapter attempts to lay out a review of literature related to big data. It examines the implication that the big data pool potentially influences ideas and policies to achieve SDGs. Also, different techniques associated with collecting big data and an assortment of significant data sources are analysed in the context of achieving sustainable economic development and growth.

Findings: This chapter presents a list of challenges linked with big data analytics in governance and achievement of SDG. Different ways to deal with the challenges in using big data will also be addressed.

Big data poses as a valuable opportunity to further improve decision making in supply chain management (SCM). However, the understanding and application of big data seem rather elusive and only partially explored. The purpose of this paper is to create further guidance in understanding big data and to explore applications from a business process perspective.

This paper is based on a sequential mixed-method. First, a Delphi study was designed to gain insights regarding the terminology of big data and to identify and rank applications of big data in SCM using an adjusted supply chain operations reference (SCOR) process framework. This was followed by a questionnaire-survey among supply chain executives to elucidate the Delphi study findings and to assess the practical use of big data.

First, big data terminology seems to be more about data collection than of data management and data utilization. Second, the application of big data is most applicable for logistics, service and planning processes than of sourcing, manufacturing and return. Third, supply chain executives seem to have a slow adoption of big data.

The Delphi study is explorative by nature and the questionnaire-survey rather small in scale; therefore, findings have limited generalizability.

The findings can help supply chain managers gain a clearer understanding of the domain of big data and guide them in where to deploy big data initiatives.

This study is the first to assess big data in the SCOR process framework and to rank applications of big data as a mean to guide the SCM community to where big data is most beneficial.