Towards resilient water supply in centralized control and decentralized execution mode

open access articleThis paper shares a vision that sustainable water supply requires resilient water infrastructures which are presumably in the centralized control and decentralized execution (CCDE) mode with multiscale resilience. The CCDE should be planned based on the multiscale structure of water infrastructures, in which the systems are divided into a number of hierarchically organized subsystems. The CCDE allows independent execution of all subsystems under normal situations yet coordination of subsystems at different scales to mitigate any disturbances during failure events, i.e. the multiscale resilience. This vision is discussed in detail for water distribution systems (WDSs). Specifically, the conceptual design of the multiscale CCDE is described, and progress on understanding the multiscale structures in WDSs is summarized based on the literature review. Furthermore, a few theories consistent with the multiscale CCDE concept are discussed which include the decomposition theorems, fractal theory, control theories, and complex network theory. The next step in the vision will be to identify the optimal multiscale structure for the CCDE based on the best trade-off of different goals of WDS analysis and management. This process needs support from not only innovative modelling tools and extensive datasets and theories but also inspiring exemplar systems, e.g. natural systems

Optimal Scheduling of Variable Speed Pumps Using Mixed Integer Linear Programming -- Towards An Automated Approach

This article describes the methodology for formulating and solving optimal pump scheduling problems with variable-speed pumps (VSPs) as mixed integer linear programs (MILPs) using piece-linear approximations of the network components. The water distribution network (WDN) is simulated with an initial pump schedule for a defined time horizon, e.g. 24 hours, using a nonlinear algebraic solver. Next, the network element equations including VSPs are approximated with linear and piece-linear functions around chosen operating point(s). Finally, a fully parameterized MILP is formulated in which the objective is the total pumping cost. The method was used to solve a pump scheduling problem on a a simple two variable speed pump single-tank network that allows the reader to easily understand how the methodology works and how it is applied in practice. The obtained results showed that the formulation is robust and the optimizer is able to return global optimal result in a reliable manner for a range of operating points.Comment: Presented at 19th Computing and Control for the Water Industry Conference, CCWI 202

Facilitating Deep Learning for Edge Computing: A Case Study on Data Classification

https://attend.ieee.org/dsc-2022/sicsa-event/Deep Learning (DL) is increasingly empowering technology and engineering in a plethora of ways, especially when big data processing is a core requirement. Many challenges, however, arise when solely depending on cloud computing for Artificial Intelligence (AI), such as data privacy, communication latency, and power consumption. Despite the elevating popularity of edge computing, its overarching issue is not the lack of technical specifications in many edge computing platforms but the sparsity of comprehensive documentation on how to correctly utilize hardware to run ML and DL algorithms. Due to its specialized nature, installing the full version of TensorFlow, a common ML library, on an edge device is a complicated procedure that is seldom successful, due to the many dependent software libraries needed to be compatible with varying architectures of edge computing devices. Henceforth, in this paper, we present a novel technical guide on setting up the TensorFlow Lite, a lightweight version of TensorFlow and demonstrate a complete workflow of model training, validation, and testing on the ODROID-XU4. Results are presented for a case study on energy data classification using the outlined model show almost 7 times higher computational performance compared to cloud-based AI

Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation

open access articleThis data article describes a dataset collected in 2022 in a domestic household in the UK. The data provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights from the data using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plus and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (C), relative indoor humidity (RH%), and occupancy (binary). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems

Analyzing Domestic Energy Behavior with a Multi-Dimensional Appliance-Level Dataset

Data, in its purest nature, has an authority on the systems it accompanies by feeding an accurate representation of the observed reality. In energy efficiency, the underlying motivation for big data efforts revolves around the intrinsic need to understand end-user electric energy consumption and means to improve it. Hence, developing a rich, detailed, and realistic power consumption dataset entails a deliberate process of preparing the data collection environment, configuring proper Internet of Energy (IoE) sensors and managing the collected data. In this work, a novel power consumption dataset is presented in efforts to improve the state-of-the-art of energy efficiency research in buildings. The dataset is also accompanied by a two-dimensional (2D) counterpart produced using Gramian Angular Fields (GAF) that creates pictorial summaries from one-dimensional (1D) data. Data acquisition is carried out using the ODROIDXU4 edge computing hub, Home Assistant software, and a collection of smart plugs and sensors. A notable use case is presented to signify the merits of the data and its analysis tools to achieve computationally efficient classification

A Modular Recommender System for Domestic Energy Efficiency

Recommender systems continually impact multiple verticals by introducing automated intelligence to decision making. When applying such Artificial Intelligence (AI) tools to energy efficiency problems, a number of opportunities and challenges present themselves. This paper presents a modular recommender system for improving domestic household energy savings. The recommender relies upon a contextual appliance-level energy dataset from seven appliances in a household. Modularity is incorporated into the system design to create customizable sub-components that adapt to the nature of the data and the end-user’s preference, such as modules that recommend based on usage patterns, power consumption, and occupancy. Machine Learning (ML) has been used for automatic appliance profiling and rank-based methods are employed to evaluate the recommender based on relevance scores. Implementation results for generating recommendations for two weeks yield a Root Mean Square Error (RMSE) of 0.2288, Normalized Cumulative Discounted Gain (NCDG) of 0.729 for seven appliances. Future work includes evaluation on edge computing platforms and user testing through a mobile application

Reliable, resilient and sustainable water management: The Safe & SuRe approach

Global threats such as climate change, population growth, and rapid urbanization pose a huge future challenge to water management, and, to ensure the ongoing reliability, resilience and sustainability of service provision, a paradigm shift is required. This paper presents an overarching framework that supports the development of strategies for reliable provision of services while explicitly addressing the need for greater resilience to emerging threats, leading to more sustainable solutions. The framework logically relates global threats, the water system (in its broadest sense), impacts on system performance, and social, economic, and environmental consequences. It identifies multiple opportunities for intervention, illustrating how mitigation, adaptation, coping, and learning each address different elements of the framework. This provides greater clarity to decision makers and will enable better informed choices to be made. The framework facilitates four types of analysis and evaluation to support the development of reliable, resilient, and sustainable solutions: “top‐down,” “bottom‐up,” “middle based,” and “circular” and provides a clear, visual representation of how/when each may be used. In particular, the potential benefits of a middle‐based analysis, which focuses on system failure modes and their impacts and enables the effects of unknown threats to be accounted for, are highlighted. The disparate themes of reliability, resilience and sustainability are also logically integrated and their relationships explored in terms of properties and performance. Although these latter two terms are often conflated in resilience and sustainability metrics, the argument is made in this work that the performance of a reliable, resilient, or sustainable system must be distinguished from the properties that enable this performance to be achieved

Elevating Energy Data Analysis with M2GAF: Micro-Moment Driven Gramian Angular Field Visualizations

open access proceedingsWith global pollution and buildings power consumption on the rise, energy efficiency research has never been more necessary. Accordingly, data visualization is one of the most sought challenges in data analysis, especially in energy efficiency applications. In this paper, a novel micro-moment Gramian angular fields time-series transformation of energy signals and ambient conditions, abbreviated as M2 GAF, is described. The proposed tool can be used by energy efficiency researchers to yield a deeper understanding of building energy consumption data and its environmental conditions. Current results show sample G2 GAF representation for three power consumption datasets. In summary, the proposed tool can unveil novel energy time-series analysis possibilities as well as original data visualization that can yield deeper insights, and in turn, improved energy efficiency

Edge Deep Learning for Smart Energy Applications

The Internet of Energy (IoE) paradigm is an advancing area of research concerning the fusion of smart technology and energy efficiency [1], combing data collection, processing, and visualization. Smart energy monitoring witnesses technological advancements such as smart metering and IoE networking, allowing the expansion of smart energy networks in a smart house. In this research, we aim to understand energy behavior through big data collection and classification and improve energy efficiency using behavioral economics, deep learning-based recommender systems, and intuitive data visualizations. In specific, a specialized case study is reported on the ODROID XU4 platform [3], and a setup developed at De Montfort University (DMU) at the Energy Lab and AI Lab, it is aimed to build a novel appliance level dataset with contextual ambient environmental data. As a novel advancement in the field, the ODROID performs edge deep learning computations on the collected data, to clean it, summarize it, anonymize it, and classification, it transmits it to a cloud server for further deep processing and storage. Concluding, the proposed work provides aids in exploiting energy-efficiency technologies for improving energy efficiency via an innovative, automated energy efficiency deep learning engine

Seismic Performance Assessment of Water Distribution Systems Based on Multi-Indexed Nodal Importance

open access articleSeismic performance assessment of water distribution systems (WDSs) based on hydraulic simulation is essential for resilience evaluation of WDSs under earthquake disasters. The assessment is mainly to determine how the water supply will be affected due to pipe breaks caused by the earthquake, with the water supply loss estimated based on the loss of supply to nodes. Existing research works usually use the average or overall performance metric of all user nodes as the system performance indicator without considering user nodes' individual performance and criticality. This paper proposes a framework to evaluate the importance of user nodes considering post-earthquake rescue service and the seismic performance of individual user nodes in the WDS, which supports the pipeline renovation plan to improve the performance of critical user nodes. The importance of user nodes is evaluated by a multi-index model, including the indices for daily service, post-earthquake rescue service, and network topology influence of user nodes. These indices evaluate the importance of user nodes in terms of their roles for daily water service, emergent rescue service, and water transmission to other nodes, respectively. Fragility model of pipelines evaluates the earthquake-induced damages of the WDS, and the seismic performance assessment of the WDS system is performed by the hydraulic model of the WDS with pipeline damages. The proposed framework is implemented in an actual WDS; the results show that the importance classification to user nodes by multi-index approach can identify the critical user nodes for post-earthquake rescue service, which traditional methods may ignore. The importance classification and seismic performance of individual user nodes make it feasible to check the seismic performance of critical user nodes and formulate a targeted pipeline renovation plan to focus limited resources on critical user nodes