Statistical methodology to assess changes in the electrical consumption profile of buildings

Abstract

Many efforts have been made to define patterns, predict, and forecast energy use. However, changes in energy consumption may be studied in detail using various methodologies. This work presents a statistical methodology to assess changes in a facility consumption profile. Consumption patterns are obtained from a historical database of a predefined time interval, according to the type of day (day of the week, working or non-working), and an index that assesses change in the electrical consumption profile is proposed. Assessing these changes enables associating these values with possible events in a facility, which can serve to generate alarms in an energy management system, and reduce costs and maintenance periods. Additionally, a multi-criteria interpretation of the applied test table is presented that offers explanations and identifies possible causes of anomalous consumption.

A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles

“…The SAICC methodology was first developed by the authors in a previous study. It finds patterns and statistically quantifies the changes in an ECP [6]. This methodology establishes conclusions that give clues about the possible causes of changes in electricity consumption.…”

“…(2) obtention of consumption patterns; (3) statistical quantification; and (4) presentation of results [6]. Figure 7 shows a simplified block diagram of the SAICC methodology, where the transformation of time-series data is carried out through three different methods: detrending method, seasonal filter, and SAEC.…”

“…In a previous work [6], the authors presented a novel SAICC (statistical assessment for identifying changes in consumption) methodology to assess the changes in the ECP of buildings. Nonetheless, the effect of time-series treatment in the detection of anomalies was not analyzed.…”

“…The results of the above-mentioned time-series treatment methods are compared using the SAICC methodology [6]. Patterns are obtained in two different scenarios: (i) without performing time-series treatment; and (ii) using three different methods for handling the time-series components, where one method (SAEC) is proposed by the authors in this work.…”

Characterization and Classification of Daily Electricity Consumption Profiles: Shape Factors and k-Means Clustering Technique

“…In this case, the number of characteristics to represent each daily ECP is 96, since each data corresponds to the measurements average taken during every 15 minutes of an entire day. It is necessary to define time ranges and weekdays according to the electric tariff and working patterns as a previous step to classify the ECPs [10]. Table 1 shows the time ranges and the number of measurements in each time period for commercial and industrial consumers according to Ecuadorian regulations.…”