Developing a simple software tool for the analysis of high resolution data from ‘smart’ utility meters

Graeme Stuart, Dr Simon Taylor, and Professor Paul Fleming, Institute of Energy and Sustainable Development, De Montfort University

Keywords

analysis, buildings, energy management, metering, empirical modelling, utilities, water

Abstract

Energy management is used by organisations to monitor and control the environmental and financial costs of energy usage. It involves monitoring performance, identifying potential interventions and assessing their effectiveness. A key element of an effective regime is access to good quality data. High resolution utility consumption data (typically electricity, gas and water) is becoming widely available. Leicester City Council has access to such data in over 200 municipal buildings.

The data contains details of the effects of both interventions and previously unidentified events. Appropriate data analysis techniques can extract these details to provide a simple and intuitive view of the usage behaviour, although the process can be time consuming and complicated.

A typical analysis follows three main steps.

Producing an initial model of consumption using all the available data.
Subdividing the model into internally consistent, contiguous periods.
Assessing each distinct period of consistent consumption.

Developing a simple software tool to perform an analysis using this framework involves making compromises between complexity, resolution and generality. Designing the user interface introduces compromises between usability, training requirements and effectiveness.

A case study shows that implementing the analysis framework in a simple tool provides significant insight into consumption history and can quickly quantify the effects of interventions and identify hitherto unknown events.

Such a tool has the potential to improve the effectiveness of energy management by providing a rapid assessment of energy usage patterns. Identifying unexpected increases in consumption can trigger investigations that can lead to timely remedial action, quantifying the effect of interventions is critical in the assessment of their success. Future possibilities include exception reporting, ensuring high quality results with minimum input of time.

The tool is presented as a proof of concept for the basic analysis framework in a way that can be implemented easily to provide detailed analysis of data wherever it is available in an appropriate format. The potential to provide energy management information covering significant consumption is promising.