Smart Energy Monitoring System Research Articles

Automatic Detection of Manufacturing Equipment Cycles Using Time Series

Abstract Manufacturing industry companies are increasingly interested in using less energy in order to enhance competitiveness and reduce environmental impact. To implement technologies and make decisions that lead to less energy demand, energy/power data are required. All too often, however, energy data are either not available, or available but too aggregated to be useful, or in a form that makes information difficult to access. Attention herein is focused on this last point. As a step toward greater energy information transparency and smart energy-monitoring systems, this paper introduces a novel, robust time series-based approach to automatically detect and analyze the electrical power cycles of manufacturing equipment. A new pattern recognition algorithm including a power peak clustering method is applied to a large real-life sensor data set of various machine tools. With the help of synthetic time series, it is shown that the accuracy of the cycle detection of nearly 100% is realistic, depending on the degree of measurement noise and the measurement sampling rate. Moreover, this paper elucidates how statistical load profiling of manufacturing equipment cycles as well as statistical deviation analyses can be of value for automatic sensor and process fault detection.

Employing electricity-consumption monitoring systems and integrative time-series analysis models: A case study in Bogor, Indonesia

The Paris Agreement calls for maintaining a global temperature less than 2°C above the pre-industrial level and pursuing efforts to limit the temperature increase even further to 1.5°C. To realize this objective and promote a low-carbon society, and because energy production and use is the largest source of global greenhouse-gas (GHG) emissions, it is important to efficiently manage energy demand and supply systems. This, in turn, requires theoretical and practical research and innovation in smart energy monitoring technologies, the identification of appropriate methods for detailed time-series analysis, and the application of these technologies at urban and national scales. Further, because developing countries contribute increasing shares of domestic energy consumption, it is important to consider the application of such innovations in these areas. Motivated by the mandates set out in global agreements on climate change and low-carbon societies, this paper focuses on the development of a smart energy monitoring system (SEMS) and its deployment in households and public and commercial sectors in Bogor, Indonesia. An electricity demand prediction model is developed for each device using the Auto-Regression eXogenous model. The real-time SEMS data and time-series clustering to explore similarities in electricity consumption patterns between monitored units, such as residential, public, and commercial buildings, in Bogor is, then, used. These clusters are evaluated using peak demand and Ramadan term characteristics. The resulting energy-prediction models can be used for low-carbon planning.

A Survey of Communications and Networking Technologies for Energy Management in Buildings and Home Automation

With the exploding power consumption in private households and increasing environmental and regulatory restraints, the need to improve the overall efficiency of electrical networks has never been greater. That being said, the most efficient way to minimize the power consumption is by voluntary mitigation of home electric energy consumption, based on energy-awareness and automatic or manual reduction of standby power of idling home appliances. Deploying bi-directional smart meters and home energy management (HEM) agents that provision real-time usage monitoring and remote control, will enable HEM in “smart households.” Furthermore, the traditionally inelastic demand curve has began to change, and these emerging HEM technologies enable consumers (industrial to residential) to respond to the energy market behavior to reduce their consumption at peak prices, to supply reserves on a as-needed basis, and to reduce demand on the electric grid. Because the development of smart grid-related activities has resulted in an increased interest in demand response (DR) and demand side management (DSM) programs, this paper presents some popular DR and DSM initiatives that include planning, implementation and evaluation techniques for reducing energy consumption and peak electricity demand. The paper then focuses on reviewing and distinguishing the various state-of-the-art HEM control and networking technologies, and outlines directions for promoting the shift towards a society with low energy demand and low greenhouse gas emissions. The paper also surveys the existing software and hardware tools, platforms, and test beds for evaluating the performance of the information and communications technologies that are at the core of future smart grids. It is envisioned that this paper will inspire future research and design efforts in developing standardized and user-friendly smart energy monitoring systems that are suitable for wide scale deployment in homes.