Perspectives on Dual-Purpose Smart Water Power Infrastructures for Households in Arid Regions

In hot arid climates, freshwater and power are produced simultaneously through seawater desalination since these regions receive little rainfall. This results in a unique urban water/power cycle that often faces sustainability and resilience challenges. Elsewhere, such challenges have been addressed through smart grid technologies. This chapter explores opportunities and initiatives for implementing smart grid technologies at household level for a case study in Qatar. A functional dual-purpose smart water/power nanogrid is developed. The nanogrid includes multiloop systems for on-site water recycling and on-site power generation based on sustainability concepts. A prototype dual-purpose GSM-based smart water/power nanogrid is assembled and tested in a laboratory. Results of case study implementation show that the proposed nanogrid can reduce energy and water consumptions at household level by 25 and 20%, respectively. Economic analysis shows that implementing the nanogrid at household level has a payback period of 10 years. Hence, larger-scale projects may improve investment paybacks. Extension of the nanogrid into a resilient communal microgrid and/or mesogrid is discussed based on the concept of energy semantics. The modularity of the nanogrid allows the design to be adapted for different scale applications. Perspectives on how the nanogrid can be expanded for large scale applications are outlined

Interactive visual study for residential energy consumption data

Interactive data visualization tools for residential energy data are instrumental indicators for analyzing end user behavior. These visualizations can be used as continuous home feedback systems and can be accessed from mobile devices using touch-based applications. Visualizations have to be carefully selected in order for them to partake in the behavioral transformation that end users are encouraged to adopt. In this paper, six energy data visualizations are evaluated in a randomized controlled trial fashion to determine the optimal data visualization tool. Conventional visualizations, namely bar, line, and stacked area, are compared against enhanced charts, namely spiral, heatmap, and stacked bar, in terms of effectiveness, aesthetic, understandability, and three analysis questions. The study is conducted through a questionnaire in a mobile application. The application, created through React Native, is circulated to participants in multiple countries, collecting 133 responses. From the received responses, conventional plots scored higher understandability (by 22.74%), effectiveness (by 13.44%), and aesthetic (by 10.54%) when compared with the enhanced visualizations. On the flipside, enhanced plots generated higher correct analysis questions' responses by 8% compared to the conventional counterparts. From the 133 collected responses, and after applying the unpaired t-test, conventional energy data visualization plots are considered superior in terms of understandability, effectiveness, and aesthetic. 2022 The Author(s)This paper is made possible by National Priorities Research Program, Qatar (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library.Scopu

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Enormous amounts of data are being produced everyday by sub-meters and smart sensors installed in residential buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem becoming overwhelming. Moreover, it will aid in better decision-making to reduce wasted energy and promote sustainable and energy efficient behavior. In this regard, this paper is an in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence. Specifically, an extensive survey is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted, such as machine learning algorithms, feature extraction approaches, anomaly detection levels, computing platforms and application scenarios. To the best of the authors' knowledge, this is the first review article that discusses anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumption, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, (iv) platforms for reproducibility and (v) privacy-preservation. Following, insights about current research trends are discussed to widen the applications and effectiveness of the anomaly detection technology before deriving future directions attracting significant attention. This article serves as a comprehensive reference to understand the current technological progress in anomaly detection of energy consumption based on artificial intelligence.Comment: 11 Figures, 3 Table

Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-the-art data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored

Date Pits Based Nanomaterials For Thermal Insulation Applications - Towards Energy Efficient Buildings In Qatar

Air-conditioning systems make the most significant part of energy consumption in the residential sector. There is no denying that it is essential to produce a comfortable indoor thermal environment for residents in a building. The actual goal is to achieve thermal comfort level without putting too much cost on the ecological system. An effective way to help achieve such a goal is by incorporating thermal insulation in buildings. Thermal insulations help reduce thermal energy gained during the implementation of a desired thermal comfort level. This project aims to study a new, environmentally friendly nanomaterial containing nanoparticle of date-pits to create thermal insulations. In addition, fly ash and different ratios of the nanoparticle of date pits and sand composite were investigated. Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM) were used to characterize the new materials. The material with nanoparticle of date pits and 50% by-volume epoxy provide good thermal insulation with thermal conductivity of 0.26 !$"# that may be used in existing buildings. This has the potential to reduce the overall energy consumption by 4,494 %!ℎ and thereby to reduce '() emissions of a 570 ") house by 1.8 tons annually. The use of fly ash as an insulation material was not found to be as efficient compared to nanoparticle of date pits. In conclusion, the future of using nanoparticle of date pits in construction is bright and promising due to their promising initial results.يعتبر حافظ الطاقة في المباني، من أكثر المجالات أهمية في الوقت الراهن، ويرجع ذلك لارتفاع استهلاك الطاقة في القطاع المنزلي، نتيجة لاتخدام مكيفات الهواء، ووسائل التبريد، لتأمين الارتياح الحراري في البيئة الحرارية المحيطة بالإنسان. يعد استعمال المواد العازلة حراريا في المباني من أنجح الوسائل المتبعة تقنيا و اقتصاديا وبيئياً

Comparison between variable and constant refrigerant flow air conditioning systems in arid climate: Life cycle cost analysis and energy savings

All over the world, there is a call to encourage sustainable energy thinking and implementation. There is an urgent need to consider sustainable solutions in any design projects that are able to reduce energy consumption. In the heating, ventilation, and air conditioning field, the rise of the variable refrigerant flow systems has made big progress. This study presents a life cycle cost analysis to evaluate the economic feasibility of constant refrigerant flow (CRF), and in particular, the conventional ducted unit air conditioning system and the variable refrigerant flow (VRF) system by using detailed cooling load profiles, as well as initial, operating, and maintenance costs. Two operating hours scenarios are utilized and the present worth value technique for life cycle cost analysis is applied to an existing office building located in Qatar, which can be conditioned by CRF and VRF systems. The results indicate that, although the initial cost of the VRF system is higher than that of the CRF system by 23%, the present worth cost of the VRF system is much lower than that of the CRF system at the end of the lifetime due to lower operating costs. There is also a significant energy saving of 27% by using VRF compared to the CRF. The implementation of these results on a national scale will promote the use of sustainable energy technologies such as the VRF system. 2021 by the authors. Licensee MDPI, Basel, Switzerland.Acknowledgments: The authors would like to acknowledge Al Muftah Contracting Company W.L.L. company (Doha, Qatar) for assisting this research in terms of providing useful field data and the usage of the transfer function method (TFM) through the software Hourly Analysis Program (HAP) by Carrier. The authors would also like to thank Qatar National Research Foundation (QNRF) (Doha, Qatar) for funding the open access fees for the publication of this manuscript.Scopu

POSSIBLE USAGE OF VARIABLE REFRIGERANT FLOW IN ARID CLIMATE: TECHNICAL AND MANAGEMENT PERSPECTIVE

All over the world, there is a call to encourage sustainable energy thinking and implementation. In the heating, ventilation and air conditioning field, the rise of the variable refrigerant flow systems has made a big progress throughout the years. This study presents a life-cycle cost analysis to evaluate the economic feasibility of constant refrigerant flow (CRF) in particular the conventional ducted unit air conditioning system that is widely used in Qatar and the variable refrigerant flow (VRF) system. Detailed cooling load profiles will be used for the existing units and the new VRF model in addition to initial, operating, and maintenance costs. Two operating hours scenarios are utilized to consider 12 and 24 operating hours and the present-worth value technique for life-cycle cost analysis is applied to an existing office building located in Qatar which can be conditioned by CRF and VRF systems. The results indicate that although the initial cost of the VRF system is higher than that of the CRF system, the present-worth cost of the VRF system is lower than that of the CRF system at the end of the lifetime due to lower operating costs. The implementation of these results on a national scale will promote the use of sustainable energy technologies such as the variable refrigerant flow system to reduce the energy consumption in Qatar and to improve the national power grid utilization, efficiency, and expansion in the coming years

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy. 2020, The Author(s).Open Access funding provided by the Qatar National Library. This paper was made possible by National Priorities Research Program (NPRP) grant No. 10-0130-170288 from the Qatar National Research Fund (a member of Qatar Foundation).Scopu