Examining the Impact of COVID-19 in Indonesia through Household Electricity Consumption and Modern Lifestyle

The COVID-19 pandemic had a noticeable effect on household energy consumption. In addition, modern architecture has driven growth in Indonesia's property sector in recent years and is one of the biggest energy consumers. The COVID-19 pandemic along with modern lifestyles like using advanced residential appliances have contributed to increased energy consumption in Indonesia. Homeowners do notice an impact on their electricity usage from a large-scale social restriction policy (PSBB). Predicting appliance future utilization and optimizing space are key to the energy management of residential buildings. Data collected from 150 households in Sumatra and Java Island, Indonesia, were used to compare three different house designs. The purpose of this study is to determine whether household lifestyle influences residential energy consumption. According to the analysis, household electricity consumption increased by around 11% between 2020 and 2019. eQUEST simulation analysis reveals that roof design has a small impact on reducing energy consumption. In three urban centers in Indonesia: Batam, Semarang, and Jakarta, it did not show a significant reduction in electricity consumption. The largest contributor to energy consumption patterns is household habits. The use of miscellaneous equipment (laptop, handphone, water pump, washing machine) and the use of air conditioners have significant effects on energy choice behavior, emphasizing the importance of building planning. Changing electricity usage behavior and water-saving management can lead to achieving energy efficiency targets in residential buildings. © 2023 by authors, all rights reserved.

Impact of COVID-19 on energy consumption in a residential complex in Hyderabad, India

When the Indian government declared the first lockdown on 25 March 2020 to control the increasing number of COVID-19 cases, people were forced to stay and work from home. The aim of this study is to quantify the impact of stay-at-home orders on residential Air Conditioning (AC) energy and household electricity consumption (excluding AC energy). This was done using monitored data from 380 homes in a group of five buildings in Hyderabad, India. We gathered AC energy and household electricity consumption data at a 30-min interval for each home individually in April 2019 and April 2020. Descriptive and inferential statistical analysis was done on this data. To offset the difference in temperatures for the month of April in 2019 and 2020, only those weekdays were selected where the average temperature in 2019 was same as the average temperature in 2020. The study establishes that the average number of hours the AC was used per day in each home increased in the range 4.90–7.45% depending on the temperature for the year 2020. Correspondingly, the overall AC consumption increased in the range 3.60–4.5%, however the daytime (8:00 AM to 8:00 PM) AC energy consumption increased in the range 22–26% and nighttime (8:00 PM to 8:00 AM) AC energy consumption decreased by 5–7% in the year 2020. The study showed a rise in household electricity consumption of about 15% for the entire day in the year 2020. The household electricity consumption increased during daytime by 22- 27.50% and 1.90- 6.6% during the nighttime. It was observed that the morning household electricity peak demand shifted from 7:00 AM in 2019 to 9:00 AM in 2020. Conversely, the evening peak demand shifted from 9:00 PM in 2019 to 7:00 PM in 2020. An additional peak was observed during afternoon hours in the lockdown. © 2022, The Author(s).

Household Electricity Consumer Classification Using Novel Clustering Approach, Review, and Case Study

There is an increasing demand for electricity on a global level. Thus, the utility companies are looking for the effective implementation of demand response management (DRM). For this, utility companies should know the energy demand and optimal household consumer classification (OHCC) of the end users. In this regard, data mining (DM) techniques can give better insights and support. This work proposes a DM-technique-based novel methodology for OHCC in the Indian context. This work uses the household electricity consumption (HEC) of 225 houses from three districts of Maharashtra, India. The data sets used are namely questionnaire survey (QS), monthly energy consumption (MEC), and tariff orders. This work addresses the challenges for OHCC in energy meter data sets of the conventional grid and smart grid (SG). This work uses expert classification and clustering-based classification methods for OHCC. The expert classification method provides four new classes for OHCC. The clustering method is employed to develop eight different classification models. The two-stage clustering model, using K-means (KM) and the self-organizing map (SOM), is the best fit among the eight models. The result shows that the two-stage clustering of the SOM with the KM model provides 88% of overlap-free samples and 0.532 of the silhouette score (SS) mean compared to the expert classification method. This study can be beneficial to the electricity distribution companies for OHCC and can offer better services to consumers.

A statistical and predictive modeling study to analyze impact of seasons and covid-19 factors on household electricity consumption

Load is dynamic in nature and changing from aggregated load to disaggregated loads. Hence, need to analyze individual household’s energy consumption pattern. Many factors are contributing to household electricity consumption (HEC). The most influencing factor is the end user’s behavioral aspect. The calendar and seasonal factors are directly affecting user’s behavior activities. This paper consists of two aim, first aim is to validate the performance of traditional predictive models and second aim is to identify the best-fitted predictive model from five predictive models namely: Random Forest, Linear Regression, Support Vector Machine, Neural Network (NN) and Adaptive Boosting. The orange tool is used to simulate the predictive models. The JASP tool is used for statistical analysis of the dataset. From the predictive modeling study, the NN model is the most fitted model. The values of the performance matrix parameter like MSE, RMSE and MAE of the NN model is observed to be 0.558, 0.747 and 0.562 respectively. This study gives insights to researchers and utility companies about traditional predictive models that can predict the HEC under anomaly situations like Covid-19. This study also helps the researchers in using Orange and JASP tool to perform the statistical and predictive modeling. © 2021 Published by peer-reviewed open access scientific journal.

Entropy-Based Anomaly Detection in Household Electricity Consumption

Energy efficiency is one of the most important current challenges, and its impact at a global level is considerable. To solve current challenges, it is critical that consumers are able to control their energy consumption. In this paper, we propose using a time series of window-based entropy to detect anomalies in the electricity consumption of a household when the pattern of consumption behavior exhibits a change. We compare the accuracy of this approach with two machine learning approaches, random forest and neural networks, and with a statistical approach, the ARIMA model. We study whether these approaches detect the same anomalous periods. These different techniques have been evaluated using a real dataset obtained from different households with different consumption profiles from the Madrid Region. The entropy-based algorithm detects more days classified as anomalous according to context information compared to the other algorithms. This approach has the advantages that it does not require a training period and that it adapts dynamically to changes, except in vacation periods when consumption drops drastically and requires some time for adapting to the new situation.

The impact of the COVID-19 on households' hourly electricity consumption in Canada.

The spread of the COVID-19 pandemic caused a tremendous impact on our societies, including changes in household energy consumption. Using measured electricity use data from 500 homes in Ottawa, Canada, this study applies changepoint analysis, descriptive statistics, k-means clustering, and the corresponding change of electricity utility bills before and after COVID-19. Our analysis indicates that the average household daily electricity consumption increased by about 12% in 2020 relative to 2019, about one-third was due to warmer temperatures, with much of the rest due to the temperature-independent loads (e.g., lighting and appliances). Additionally, the highest five peak loads corresponding to post-COVID are significantly higher (15-20%) than peaks that occurred pre-COVID. The lockdown's impact on household electricity use is not consistent, and there are noticeable differences among different months, seasons, and day types. Two clusters of household electricity use patterns emerged, with about one-third showing significant increases during the pandemic and the remainder showing only minor changes. On the other hand, in the summer, all customers' electricity use profile patterns after the pandemic resemble the pattern before the pandemic. Yet, there is a significant increase (from 16.3 to 29.1%) in daily demand after COVID-19. Finally, the average increase in the utility bill post-COVID would be 9.71% if TOU rates were used instead of the flat rate that was implemented as a subsidy to consumers.