Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

Abstract

Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its effectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s.

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

“…REDD (Reference Energy Disaggregation Data Set) and UK-DALE (UK Domestic Appliance-Level Electricity) datasets were selected due to their consistency and low granularity, which enable the appliance status to be captured with a high degree of precision. Details about the impacts of the time granularity in electricity metering can be found in [22][23][24]. However, as we are going to discuss later, the proposed analysis is also valid for other time granularities; the only necessary condition is the time synchronization of the time series.…”

“…The REDD dataset contains measurements from six different households in the United States with a 3-s granularity, with monitoring periods from 2.7 to 25 days. The monitoring periods are not the best for detecting utilization patterns (unfortunately, 2.7 days can include some holidays, for example, and even 25 days do not cover different seasons of the year), but on the other hand this dataset records a good quantity of individual channels (18,9,20,18,24,15), each of them representing one individual appliance. The information in Table 1 leads to a system state domain of high dimension: R 52 , R 18 , R 4 , R 5 , and R 24 and R 18 , R 9 , R 20 , R 24 , and R 15 , respectively, for the UK-DALE and the REDD datasets.…”

“…For the REDD dataset, with one sample every 3 s, the set of system statuses has 28,800 measurements for each monitored day. In addition, if each system status is represented by a binary vector that indicates the status of each appliance (i.e., on or off, or 1 or 0), the number of possible statuses for each house is 2 52 , 2 18 , 2 4 , 2 5 , and 2 24 for the UK-DALE and 2 18 , 2 9 , 2 20 , 2 24 , and 2 15 for the REDD dataset. These numbers render traditional statistical analysis limited.…”

Design and Testing of a Power Analyzer Monitor and Programming Device in Industries with a LoRA LPWAN Network

“…Sanchez-Sutil et al [24] implemented a smart meter for monitoring electrical consumption with a high resolution and sending data through wireless technologies and cloud storage. Hernandez et al [25] studied the influence of granularity on measurements made with wirelessly connected smart meters.…”

Pass/Fail Quality Assessment in Last Mile Smart Metering Networks Based on PRIME Interface

“…The duration of the pending state will be shorter if the SMs are read more frequently, which is likely to happen in the near future. It is shown in [22] that frequent readings allow describing household consumption profile features with greater accuracy. The need for more frequent readings can also be caused by the development of distributed generation and energy storage systems.…”