Academic literature on the topic 'Dubai Electricity and Water Authority (DEWA)'

Purpose The purpose of this paper is to investigate a benchmarking project carried out by the Dubai Electricity and Water Authority (DEWA) as part of a structured benchmarking initiative. The project was based on the TRADE benchmarking methodology and this paper examines the tools, activities and outcomes that relate to each stage of the adopted methodology. Design/methodology/approach This study is based on case study methodology. Data were collected from various sources including analysis of project reports written by DEWA’s benchmarking team reporting on their activities during the project. Data were also collected from four project presentations given at different stages of the project. In addition, the research team held three meetings with the DEWA benchmarking team at different stages of the benchmarking project. Findings The results show the key challenges and successes faced during each stage of the benchmarking project. It indicates the actions taken to overcome the challenges and the role played by internal and external stakeholders in facilitating the success of the benchmarking project. Practical implications The study presents information that would guide organisations that wish to carry out a benchmarking project – and particularly those implementing benchmarking for the first time. The study provides a summary of the key lessons learnt by DEWA’s benchmarking team as a guide for other organisations. Originality/value Academic research has not adequately examined and analysed the stage-by-stage elements of a benchmarking project from the perspective of the implementing organisation. This study addresses this gap by detailing and analysing the experiences of a benchmarking project by tracking the stage-by-stage activities of the benchmarking team.

Abstract UAE is marked by the increasing demand for water and electricity due to demographic, environmental and economic factors, coupled with the dependence on water desalination process, which is costly, consumes a lot of energy and is non-environmentally friendly. Like most of the authorities in UAE, Dubai Electricity and Water Authority is facing the challenges of balancing supply with demand and responding to consumer requirements, from one side, and addressing the continuously increasing consumption and slowing it down from another side. Therefore, policy makers can benefit from statistical data analysis in order to make informed decisions. This study aims to equip decision makers with useful tools and analysis to address some of their short- and long-term objectives related to production and consumption. The current study focused on three main objectives: (i) analysing the production of the desalination plants in Dubai, (ii) comparing the consumptions of water and electricity based on the four categories residential, commercial, industrial and others, and (iii) segmenting the various communities in Dubai depending on their consumption behavior. The data used for this study is collected from the open government data and SAS Programming is adopted for data analysis. The results of the analysis revealed that the desalinated water production follows an upward trend, yet still not in line with the consumption growth. Furthermore, there are significant differences between the four categories for both water and electricity consumptions. The highest levels of consumptions are associated with the residential and commercial categories. Finally, the cluster analysis technique revealed fifteen clusters of communities depending on the consumption levels.

The building sector consumes as much as 80% of generated electricity in the UAE; during the COVID-19 pandemic, the energy consumption of two sub-sectors, i.e., commercial (50%) and residential (30%), was significantly impacted. The residential sector was impacted the most due to an increase in the average occupancy during the lockdown period. This increment continued even after the lockdown due to the fear of infection. The COVID-19 pandemic and its lockdown measures can be considered experimental setups, allowing for a better understanding of how users shift their consumption under new conditions. The emergency health measures and new social dynamics shaped the residential sector’s energy behavior and its increase in electricity consumption. This article presents and analyzes the identified issues concerning residential electricity consumers and how their behaviors change based on the electricity consumption data during the COVID-19 period. The Dubai Electricity and Water Authority conducted a voluntary survey to define the profiles of its residential customers. A sample of 439 consumers participated in this survey and four years of smart meter records. The analysis focused on understanding behavioral changes in consumers during the COVID-19 period. At this time, the dwellings were occupied for longer than usual, increasing their domestic energy consumption and altering the daily peak hours for the comparable period before, during, and after the lockdown. This work addressed COVID-19 and the lockdown as an atypical case. The authors used a machine learning model and the consumption data for 2018 to predict the consumption for each year afterward, observing the COVID-19 years (2020 and 2021), and compared them with the so-called typical 2019 predictions. Four years of fifteen-minute resolution data and the detailed profiles of the customers led to a better understanding of the impacts of COVID-19 on residential energy use, irrespective of changes caused by seasonal variations. The findings include the reasons for the changes in consumption and the effects of the pandemic. There was a 12% increase in the annual consumption for the sample residents considered in 2020 (the COVID-19-affected year) as compared to 2019, and the total consumption remained similar with only a 0.2% decrease in 2021. The article also reports that machine learning models created in only one year, 2018, performed better by 10% in prediction compared with the deep learning models due to the limited training data available. The article implies the need for exploring approaches/features that could model the previously unseen COVID-19-like scenarios to improve the performance in case of such an event in the future.

PurposeThe main purpose of the Dubai Electricity and Water Authority (DEWA) sustainability case is to allow students to explore how nonfinancial information reported in sustainability plays a vital role in maintaining a trade-off between current economic pressure and future environmental needs.Design/methodology/approachThis is an exploratory study in nature using a qualitative case study approach. The case requires an examination of DEWA's sustainability reporting (SR) in the context of Global Reporting Initiatives (GRIs). This case is designed to assist students in gauging DEWA's sustainability and explore how the company evaluates the materiality of sustainability issues.FindingsWith stakeholders' and investors' increased interest in sustainability, the authors argue that accounting programs should incorporate this topic into their curricula. The case enables students to focus on sustainability-related initiatives with DEWA that are aligned with GRI initiatives. The case might be instructive for both undergraduate and postgraduate students studying environmental and management accounting.Originality/valueThis case study is the first of its kind in the Gulf Cooperation Council (GCC) region to comprehensively analyze DEWA's sustainability practices concerning GRI-based SR. This study widens the understanding of DEWA's implementation of GRI standards in the preparation of its sustainability reports.

This paper presents a study on ranking of 27 identified innovation related factors which influencing Artificial Intelligence (AI) performance in UAE. These factors were clustered into four groups namely process innovation; management capabilities; personal expertise and organization structure. A questionnaire was designed based on these factors where respondents were required to gauge the influence of these factors using Likert scale. Respondents were from three UAE organisations which are Dubai Police, Dubai Electricity & Water Authority Dewa, and Emirates Integrated Telecommunications Company. A total of 384 valid responses from the questionnaire survey were analysed for its reliability using Cronbach alpha criterion. Then the data was analysed for its mean score and standard deviation values for each of the factors to determine the rank of each factor and also its influence on the organisation performance. The study foundthat the mean scores for the factors are in the range from 2.90 to 3.56 while for the average mean score for the groups are from 3.15 to 3.44. the study also found that two factors which are management capabilities and artificial intelligence are highly influencing the organisational performance while the other three factors which process innovation, personal expertise and organisational structure are moderately influencing the UAE organisational performance. The findings of this study will ably provide a better understanding of innovation related factors and how it affects overall performance.

Abstract Soiling of photovoltaics (PV) panels is affected by various factors such as relative humidity, dust concentration, and panel tilt angle. The soiling can lead to significant losses in electricity production, especially in a place like Dubai, UAE. Soiling can also lead to long-term damage of the PV panels such as degradation and delamination due to the hot spots caused by dirt deposition. It is important to choose the right cleaning strategy (method and frequency) to maximize the electricity production and economic performance of the PV facility. An optimization algorithm was developed and tested for multiple PV panel configurations based in Dubai Water and Electricity Authority’s (DEWA) outdoor test facility (OTF) solar lab. The algorithm’s input included electricity production, soiling rates (SRs), electricity price, and cleaning costs. The output included number of cleaning events and the extra revenue as compared with the current practice of periodic (5-day cycle) manual cleaning. Four different cleaning scenarios were tested and compared with the current scenario. Three scenarios resulted in improved net cost benefit (NCB), up to 34% for the case of performance-based manual cleaning. The fourth scenario resulted in diminished NCB, down by 245% for the case of daily automatic cleaning. Other findings of the study included higher tilt angles that resulted in lower cleaning requirements and thin-film PV panels that required less cleaning than first generation PV panels (mono/polycrystalline). The algorithm is an effective yet simple tool to help operators optimize the NCB of their PV facilities.

Organisations paid more attentionto the innovations of artificial intelligence (AI)technology to improve the organizational performance. Hence, using AI-related innovations to support the organizationrequires understanding of factors affecting organizational performance. Thus, this paper presents the development of PLS-SEMmodelof AI-related innovations factors that affect the organisational performance. The study identified 21innovation AI-related factors that were clustered into four groups namely process innovation; management capabilities; personal expertise and organization structure.The model comprised of fourexogenous constructs of the innovationfactors and one endogenous construct of organisational performance. The data used to develop the model was derived from 384valid responses of a questionnaire survey amongst the employees of three government organizations in Dubai, which are Dubai Police, Dubai Electricity & Water Authority Dewa, and Emirates Integrated Telecommunications Company. The survey adopted simple random sampling technique in respondents’ selection. The model was developed in SmartPLS software and was evaluated at the measurement and structural components of the model. It was found that the modelhas achieved its goodness-of-fit, GoF criteria of0.596which indicates that the model has substantial validating power. The hypothesis testing results found that three out of four relationships are significant which are having t-value and p-value above the cut-off values. The significant relationships are organization structure, personal expertise and process innovation. However, the unsignificant relationship ismanagement capabilities affecting the organisational performance. This is due to the characteristics of the collected data which is not strong enough to establish significant relationship as what have been hypothesized.The findings are contributions to any parties that involved in the application of AI innovation to improve organisational performance.