User-Centred Scalable Big Data Visualizer for Power Consumption Data in the Electrical Secondary Distribution Network

Establishment of Smart Grids for electrical power has been practised worldwide for the purpose of bringing reliability, security, and efficient management of electrical power networks for enhancing quality service to the society. Apart from the potential aim, smart grid has been a challenge to developing countries, including Tanzania from cost and technology point of view. Due to the use of many smart equipment involved in smart grids like Advanced Metering Infrastructure (AMI) equipped with smart meters and sensors, handling and managing big data has been a challenge. Among the challenges is the issue of visualizing the Big Data due to big volume generated with high velocity. This paper is developing a user-centered scalable big data visualizer for the electrical secondary distribution network by making use of design process model by Akanmu et al. (2017) and design activity framework by McKenna et al. (2014). The approach involves three phases: pre- development, development and post-development phase. The paper reviews several approaches in visualization and demonstrates effective big data visualization. The paper managed to visualize households’ units purchased against power consumed as well as balancing visualization of transformer phases

Computational Tools for Data Processing in Smart Cities

Smart Grids provide many benefits for society. Reliability, observability across the energy distribution system and the exchange of information between devices are just some of the features that make Smart Grids so attractive. One of the main products of a Smart Grid is to data. The amount of data available nowadays increases fast and carries several kinds of information. Smart metres allow engineers to perform multiple measurements and analyse such data. For example, information about consumption, power quality and digital protection, among others, can be extracted. However, the main challenge in extracting information from data arises from the data quality. In fact, many sectors of the society can benefit from such data. Hence, this information needs to be properly stored and readily available. In this chapter, we will address the main concepts involving Technology Information, Data Mining, Big Data and clustering for deploying information on Smart Grids

Attributes of Big Data Analytics for Data-Driven Decision Making in Cyber-Physical Power Systems

Big data analytics is a virtually new term in power system terminology. This concept delves into the way a massive volume of data is acquired, processed, analyzed to extract insight from available data. In particular, big data analytics alludes to applications of artificial intelligence, machine learning techniques, data mining techniques, time-series forecasting methods. Decision-makers in power systems have been long plagued by incapability and weakness of classical methods in dealing with large-scale real practical cases due to the existence of thousands or millions of variables, being time-consuming, the requirement of a high computation burden, divergence of results, unjustifiable errors, and poor accuracy of the model. Big data analytics is an ongoing topic, which pinpoints how to extract insights from these large data sets. The extant article has enumerated the applications of big data analytics in future power systems through several layers from grid-scale to local-scale. Big data analytics has many applications in the areas of smart grid implementation, electricity markets, execution of collaborative operation schemes, enhancement of microgrid operation autonomy, management of electric vehicle operations in smart grids, active distribution network control, district hub system management, multi-agent energy systems, electricity theft detection, stability and security assessment by PMUs, and better exploitation of renewable energy sources. The employment of big data analytics entails some prerequisites, such as the proliferation of IoT-enabled devices, easily-accessible cloud space, blockchain, etc. This paper has comprehensively conducted an extensive review of the applications of big data analytics along with the prevailing challenges and solutions

Big Data in Power Systems: Leveraging grid optimization and wave energy integration

Power systems have been through different challenges and technological innovations in the last years and are rapidly evolving into digital systems through the deployment of the smart grids concept. Producing large amounts of data, power systems can benefit from the application of big data analytics which can help leveraging the optimization processes going on in power grids nowadays. The whole value of chain of electric power can benefit from the application of big data techniques. This paper presents a short overview of possible applications and challenges that still need to be considered for this synergy to grow. Under the framework of an H2020 funded project named BigDataOcean, a case study will be described, showing how a data-driven approach can foster the development of offshore renewable sources using the example of wave energy

Cloud-based IoT Analytics for the Smart Grid: Experiences from a 3-year Pilot

The transformation of electrical grids into smart-grid is seen as one of the major technological challenges of our times and at the same time as one of the key domains for Internet of Things (IoT). Smart-home technologies and corresponding analytics are an integral part of many use cases in this field. In this paper we present a cloud-based test bed for capturing and analyzing smart-home data and report on experiences from a 3 year pilot with a cloud-based system. We discuss on real-world challenges that we encountered throughout the pilot - e.g. related to big data volumes and data quality - and describe corresponding technical solutions

Short term energy consumption forecasting using neural basis expansion analysis for interpretable time series

Smart grids and smart homes are getting people\u27s attention in the modern era of smart cities. The advancements of smart technologies and smart grids have created challenges related to energy efficiency and production according to the future demand of clients. Machine learning, specifically neural network-based methods, remained successful in energy consumption prediction, but still, there are gaps due to uncertainty in the data and limitations of the algorithms. Research published in the literature has used small datasets and profiles of primarily single users; therefore, models have difficulties when applied to large datasets with profiles of different customers. Thus, a smart grid environment requires a model that handles consumption data from thousands of customers. The proposed model enhances the newly introduced method of Neural Basis Expansion Analysis for interpretable Time Series (N-BEATS) with a big dataset of energy consumption of 169 customers. Further, to validate the results of the proposed model, a performance comparison has been carried out with the Long Short Term Memory (LSTM), Blocked LSTM, Gated Recurrent Units (GRU), Blocked GRU and Temporal Convolutional Network (TCN). The proposed interpretable model improves the prediction accuracy on the big dataset containing energy consumption profiles of multiple customers. Incorporating covariates into the model improved accuracy by learning past and future energy consumption patterns. Based on a large dataset, the proposed model performed better for daily, weekly, and monthly energy consumption predictions. The forecasting accuracy of the N-BEATS interpretable model for 1-day-ahead energy consumption with day as covariates remained better than the 1, 2, 3, and 4-week scenarios

The Internet of Things in a Smart Connected World

The internet of things (IoT) constitutes a network of embedded devices that incorporate sensors and communication functions. The IoT is becoming one of the core technologies of the Fourth Industrial Revolution. This is because the IoT creates new values in the connected smart world by collecting big data, uploading data into clouds, and processing data in intelligent systems. The newly created values in intelligent systems differ from previously generated values that were based on the simple automated systems of the Third Industrial Revolution. In this chapter, we present a brief introduction of the IoT, which connects to the Internet through incorporating sensors and communication functions in various smart objects. In the IoT era, it is possible to create a networked smart world with powerful new services and products that create new values. As applications of the IoT, we introduce smart homes, smart electronics, smart connected cars, smart grids, smart healthcare, smart wearable devices, etc. In addition, we illustrate a specific IoT complex in a smart city as one of the smart connected applications of the IoT. Finally, we describe the predicted hyper-connected smart world that will be achieved through the IoT

Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis

Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid. In the present contribution, we describe a data processing network for the in-house developed low-voltage, high-rate measurement devices called electrical data recorder (EDR). These capture units are capable of sending the full high-rate acquisition data for permanent storage in a large-scale database. The EDR network is specifically designed to serve for reliable and secured transport of large data, live performance monitoring, and deep data mining. We integrate dedicated different interfaces for statistical evaluation, big data queries, comparative analysis, and data integrity tests in order to provide a wide range of useful post-processing methods for smart grid analysis. We implemented the developed EDR network architecture for high-rate measurement data processing and management at different locations in the power grid of our Institute. The system runs stable and successfully collects data since several years. The results of the implemented evaluation functionalities show the feasibility of the implemented methods for signal processing, in view of enhanced smart grid operation. © 2015, Maaß et al.; licensee Springer