RO-Crate Time Series Exporter for the Building Consumption Data of KIT Campus North

The facility management (FM) of the Karlsruhe Institute of Technology (KIT) operates an infrastructure for measuring energy consumption to invoice other organizational units within KIT for the energy consumed. For this purpose, the measuring infrastructure automatically records and stores the energy consumption of all buildings on Campus North at a resolution of 15 minutes. The recorded and stored consumption comprises different energy types, namely electricity, gas, heat, water (warm, cooling, drinking, and several kinds of wastewater), and compressed air. Since this measurement infrastructure is already in operation since 2006, the consumption data stored as time series meanwhile cover a long period of time. The covered period of time makes these energy consumption time series highly interesting for the energy research community, especially for energy researchers at KIT. However, accessing the data is challenging. While the original infrastructure was designed for single-user access and limited data throughput, it now faces multiple users and high data throughput. Moreover, since the used technology does not scale with the ever-growing data volumes, FM finally updated the data infrastructure. However, despite improvements with regard to performance, the new data infrastructure brings new challenges, including data only partially moved to the new infrastructure. For this reason, retrieving time series whose time range spans data from both old and new infrastructure requires a researcher to write queries for both database systems, which in turn requires knowing the complicated logic of the used schemas of both databases. Even if a researcher successfully queried such a time series, she needs further queries to allow measurement units, measurement quantities, and scaling factors to be included in the interpretation of the data. Given both this challenging data access and the increasing interest in the data, we started to simplify the process of data querying by developing a web service with a simple REST (Fielding, 2000) interface. This interface allows researchers to query data in a unified way, without requiring any knowledge about the underlying databases and thereby lowering the hurdles of accessing the data. The interface requires only a time range, a list of buildings, and energy types as inputs and returns a ZIP file including the time series as CSV files and an RO-Crate (Soiland-Reyes et al., 2022) metadata file in JSON. The metadata file fully describes the requested energy consumption time series by using the RO-Crate data package standard with an extended, in-house developed profile for time series description. This RO-Crate metadata file enables an interpretation of the obtained data without any prior knowledge and reduces the burden on researchers to publish the data according to good scientific practice. Since a lot of research using energy consumption data benefits from including exogenous influences such as weather (Dannecker, 2015 & Haben et al., 2023), the developed web service also allows obtaining weather time series for the specified time range, which again is described in the RO-Crate metadata file. The present poster shows the steps taken to develop the web service: It starts with the analysis of the original database schemas, before it describes the agreement on the required information resulting in a shared database schema. The poster continues with the transformation of the original data into the shared schema that builds the data foundation of the service. Next, the poster presents the creation of the time series profile, the standards and vocabularies, the used technologies to develop the service, and the challenges during the development of the software. The poster concludes with an outlook on planned improvements and extensions of the developed web service

Implementation of cloud services by using real-time analysis to reduce energy consumption

Through the continual application of sensors, wireless networking, network communication and cloud computing technology, vast volumes of data are increasingly collected in the energy sector which needs to be utilized for effective management. In this project, the overall perspective is to analyze energy consumption data collected from households’ smart meters in London and combining it with the application of cloud data technology. I will explore and utilize a state-of-the-art cloud service infrastructure to analyze and make smart decisions on managing energy usage. There is interest in using data mining techniques and time series for machine learning modelling to deliver a predictive measurement approach for forecast consumption. The cloud service proposed is Amazon Web Services (AWS) which will be used as statistical data for daily energy use, it can collect, analyze, and implement machine learning models to learn a user’s behaviors and enhance energy efficiency by automatically alerting the user when necessary in real-time. There needs to be a warning mechanism such as a web-based and mobile application which can interact with users through energy dashboards and SMS/emails, that way alerting the user and utility companies on excess consumption which is recommended in this research

Economical Way of GPRS Based Fully Automated Energy Metering System

This paper presents a design of secure and economical (low cost) way of GPRS based fully automated energy metering system that measures and transmits the total electrical energy consumption to main server using general packet radio service (GPRS) technology provided by GSM networks and also present how the meter reading, disconnection and reconnection can be controlled from server end. The proposed EGFAEM system consist of four main parts: Energy Meters, Communication part over GPRS, Server and Management part and consumer end for billing and payment. A single phase energy meter prototype has been implemented to provide measurement up to 40A load current and 230V line to neutral voltage. Communication part is implemented by GPRS module and microcontroller, sever and consumer end are implemented in web server

An Empirical Assessment of Energy Management Information System Success Using Structural Equation Modeling

The Energy Industry utilizes Energy Management Information Systems (EMIS) smart meters to monitor utility consumers’ energy consumption, communicate energy consumption information to consumers, and to collect a plethora of energy consumption data about consumer usage. The EMIS energy consumption information is typically presented to utility consumers via a smart meter web portal. The hope is that EMIS web portal use will aid utility consumers in managing their energy consumption by helping them make effective decisions regarding their energy usage. However, little research exists that evaluates the effectiveness or success of an EMIS smart meter web portal from a utility consumer perspective. The research goal was to measure EMIS smart meter web portal success based on the DeLone and McLean Information Success Model. The objective of the study was to investigate the success constructs system quality, information quality, service quality, use, and user satisfaction, and determine their contribution to EMIS success, which was measured as net benefits. The research model used in this study employed Structural Equation Modeling (SEM) based on Partial Least Squares (PLS) to determine the validity and reliability of the measurement model and to evaluate the hypothetical relationships in the structural model. The significant validity and reliability measures obtained in this study indicate that the DeLone and McLean Information Success Model (2003) has the potential for use in future EMIS studies. The determinants responsible for explaining the variance in net benefits were EMIS use and user satisfaction. Based on the research findings, several implications and future research are stated and proposed

A survey of big data and machine learning

This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper

Automatic energy expenditure measurement for health science

Background and objective: It is crucial to predict the human energy expenditure in any sports activity and health science application accurately to investigate the impact of the activity. However, measurement of the real energy expenditure is not a trivial task and involves complex steps. The objective of this work is to improve the performance of existing estimation models of energy expenditure by using machine learning algorithms and several data from different sensors and provide this estimation service in a cloud-based platform. Methods: In this study, we used input data such as breathe rate, and hearth rate from three sensors. Inputs are received from a web form and sent to the web service which applies a regression model on Azure cloud platform. During the experiments, we assessed several machine learning models based on regression methods. Results: Our experimental results showed that our novel model which applies Boosted Decision Tree Regression in conjunction with the median aggregation technique provides the best result among other five regression algorithms. Conclusions: This cloud-based energy expenditure system which uses a web service showed that cloud computing technology is a great opportunity to develop estimation systems and the new model which applies Boosted Decision Tree Regression with the median aggregation provides remarkable results

APLIKASI MONITORING SISTEM KELISTRIKAN JTE UNILA BERBASIS BCM2835

University of Lampung (Unila), particularly the Department of Electrical Engineering (JTE) always strives to provide the best service electricity system to optimize the utilization of existing internet access. There are several electrical quantities that need to be considered and maintained its quality, so it needs to be monitored in real time, i.e.: voltage, current, cos phi, power and electrical energy consumption. Step-down transformer and a current sensor ACS712-30A are the main component for measuring voltage and current. While the values of other electrical quantities are obtained from the calculation using the Python programming based Single Board Computer BCM2835 or Raspberry Pi. Measurement and calculation results can be monitored using electrical system monitoring applications based on web. The results-based monitoring application using electrical system monitoring applications has demonstrated that the pattern of consumption of electrical energy, voltage, current, power, cos phi between the phase at JTE buildings tend to be unbalanced

Integration of Legacy Appliances into Home Energy Management Systems

The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS