Household Electricity Consumption Patterns Research Articles

AN AGENT-BASED COMPUTATIONAL MODEL ON HOUSEHOLD ELECTRICITY CONSUMPTION IN INDIAN CITIES

ABSTRACT Traditional methods of planning and managing cities often adopt a simplified linear approach to understanding their composition and functioning. These approaches have led to the creation of many undesirable and non-functional cities. However, over the last few decades, there has been a radical shift in the approach to investigating cities through the adoption of complexity science and its various concepts. Unfortunately, this paradigm shift has been limited to only a handful of developed Western nations. This study proposes an extendable agent-based computational model for assessing household electricity consumption patterns in cities of developing nations. The model is demonstrated using an urban precinct in India as a representative case. It simulates the monthly electricity consumption of an urban precinct in Nagpur by combining the significant factors impacting household electricity consumption in developing nations, with the household's electricity consumption process in relation to outdoor weather conditions, and the heterogeneity in occupants’ behavior. The predictive validation of the model using the “BehaviorSpace” tool yielded promising results, with the model consistently and accurately predicting the growth of variables and household electricity consumption patterns. The model also successfully generated multiple future scenarios, showcasing its capacity to forecast the future implications of policy interventions. The simulation results indicated that household electricity consumption in Indian cities is driven by population growth and income levels. Additionally, it suggests that widely practiced rooftop solar panel installation policies may not be a viable approach to achieving sustainability in this sector. This study finds that the “emergent urbanism” approach has the potential to revitalize the urban energy planning process through an effective framework for comprehensively understanding the phenomena and addressing its various complexities.

Investigation on air conditioning load patterns and electricity consumption of typical residential buildings in tropical wet and dry climate in India

The residential sector accounts for around 24% of the total electricity consumption in India. Recent studies show that air conditioners (ACs) have become a significant contributor to residential electricity consumption. Further, it is predicted that by 2037, the demand for ACs will increase by four times due to their affordability and availability. Not many studies have been found on residential AC usage patterns and the factors (AC load, setpoint, hours of usage) that influence household electricity consumption. This paper investigates the residential AC usage patterns and AC’s contribution to total residential electricity consumption. Twenty-five urban homes from a wet and dry climatic region of India were monitored for nine months (in 2019) to determine overall household electricity consumption patterns, AC usage, and indoor environment during summer, monsoon, and winter. Analysis of seasonal consumption patterns shows a significant difference in electricity usage between homes with ACs and homes without ACs during the summer season. The average electricity consumption for AC homes was 15.1 kWh/day during summer, 6.6 kWh/day during monsoon, and 6.1 kWh/day during the winter season. Results showed that AC alone contributed to 39% of the total household consumption in summers. The peak AC usage in all homes is observed during sleep hours which was generally between 10:00 pm and 6:00 am and the average AC runtime was 6.2 h. The average indoor temperature was recorded as 26.9 °C during the AC ON period. The AC peak load, i.e., the maximum electricity demand during the AC ON period, is 1.7 kW on average during the study period. The average annual consumption of homes with ACs was 2881 kWh, and for non-AC homes, the consumption was 2230 kWh. Findings from our analysis provide a detailed understanding of AC consumption profiles and the difference in electricity consumption characteristics between AC and non-AC homes across different seasons.

Real-Time Data Analytics for Monitoring Electricity Consumption Using IoT Technology

Rising electricity bills as a result of climate variability and new home electrical and electronic appliances are becoming a major source of concern for most end users. Consumers are typically unaware of their household electricity consumption patterns and the costs associated with them, making proper planning and budgeting difficult. Monitoring and controlling energy consumption on appliances can reduce energy costs for end-users. The Internet of Things (IoT) has the potential to provide remote monitoring and control of devices via automated monitoring and control. In this study, we propose an IoT-enabled system for monitoring and controlling energy consumption in homes to save money and make electricity more affordable to low-income people. Autonomous sensor nodes attached to power outlets collect and route power consumption data to the GSM-enabled gateway. Data aggregation is performed by the gateway for data received from all end-nodes within its coverage area. The gateway sends the data to the cloud, where real-time data analytics is performed. We also developed an algorithm that enables users to not only understand their usage patterns but also remotely control their appliances at any time. Mobile phones are used to remotely turn on and off household appliances via a machine-to-machine interface, as well as to provide real-time visualization of household energy consumption patterns.

An ensemble clustering based framework for household load profiling and driven factors identification

A two-stage framework is presented for household electricity consumption pattern mining, in which a concurrent k-means and spectral clustering (CKSC) method is used in the first stage. In the second stage, different household electricity consumption (HEC) patterns are first analyzed. Then, driven factors behind each electricity consumption pattern are discovered based on multi-nominal logistic regression. Experiment is carried out using the Irish open data, including both detailed smart meter measurements and the survey data. CKSC method efficiently divides the 4181 households into six groups. Based on that, different HEC patterns in weekdays and in weekends are extracted and discussed. Meanwhile, abnormal HEC patterns over Christmas are also discovered. The results show that the identified six groups are representative, given that the discovered HEC patterns are varied both in consumption scales and intraday volatilities. Especially, potential household groups for peak load shifting and shaving are found and further discussed. Besides, the established multi-nominal logistic regression model is statistically significant. Considering the dwelling characteristics, family characteristics and household appliances of households without smart meter readings, their HEC patterns can be inferred based on the proposed model in this study.

District household electricity consumption pattern analysis based on auto-encoder algorithm

The energy shortage is one key issue for sustainable development, a potential solution of which is the integration with the renewable energy resources. However, the temporal sequential characteristic of renewable resources is different from traditional power grid. For the entire power grid, it is essential to match the energy generation side with the energy consumption side, so the load characteristic at the energy use side is crucial for renewable power integration. Better understanding of energy consumption pattern in buildings contributes to matching different source of energy generation. Under the background of integration of traditional and renewable energy, this research focuses on analysis of different household electricity consumption patterns in an urban scale. The original data is from measurement of daily energy consumption with smart meter in households. To avoid the dimension explosion phenomenon, the auto-encoder algorithm is introduced during the clustering analysis of daily electricity use data, which plays the role of principal component analysis. The clustering based on auto-encoder gives a clear insight into the urban electricity use patterns in household. During the data analysis, several feature variables are proposed, which include peak value, valley value and average value. The distinction analysis is also conducted to evaluate the analysis performance. The study takes households in Nanjing city, China as a case study, to conduct the clustering analysis on electricity consumption of residential buildings. The analysis results can be further applied, such as during the capacity design of district energy storage.

Study on Household Electricity Consumption Patterns in Rural Area of Nepal

Study on Household Electricity Consumption Patterns in Rural Area of Nepal

Data mining based framework for exploring household electricity consumption patterns: A case study in China context

Abstract This study proposes a data mining based framework for exploring the electricity consumption patterns, which includes three consecutive stages. Firstly, electricity consumption patterns and behaviors are explored in festivals such as the Spring Festival, the Labor Day and the National Day. Secondly, seasonal electricity consumption patterns and behaviors are compared, and the relationship between temperature and electricity demand is analyzed through data visualization. Thirdly, we focus on the phenomenon of electricity consumption patterns shifting. Finally, a case study of Nanjing and Yancheng City, Jiangsu Province, China is presented. The results indicate that: (1) Volatility of electricity consumption is higher in winter and summer than in spring and autumn. (2) There are three typical load profiles during the Spring Festival, two typical load profiles during the Labor Day the National Day. (3) High temperature in summer and low temperature in winter have obvious influence on electricity consumption. However, the electricity consumption peak lags one or two days behind the temperature peak in summer, and consumers’ response time gets shorter as the frequency of temperature peaks increase. (4) The phenomenon of instability of household electricity consumption patterns is identified. 7.22% of the high volatility households transferred to low volatility households from winter to spring. 6.08% low volatility households transferred to high volatility households from summer to autumn. Finally, we proposed some suggestions for promoting energy conservation and improving energy efficiency.

Does electricity consumption improve residential living status in less developed regions? An empirical analysis using the quantile regression approach

Based on data from 1128 survey questionnaires in western China, this article analyzed the impact of several factors on residential electricity consumption. The results were as follows: (1) ECp (Electricity consumption per capita), PCI (per capita income), diversity of the electrical appliances (AD), purchase price of appliances (APC) and household size (PO) were found to be closely associated. ECp, AD and APC displayed a clearly progressive characteristic as PCI increases. (2) The main factors affecting electricity consumption were PCI, AD, APC and PO (persons). PCI, AD and APC on ECp had significantly positive effects, but the price of electricity (EP) and PO had negative effects. (3)The slope coefficients of PCI, AD and PO had the differential effect on electricity use, whereas EP and APC did not have this effect. (4) Electricity consumption and appliance use were heavily interdependent. Increased electricity consumption was essential condition for improving the living status of people in less developed regions. (5) Variations in household electricity consumption patterns reflected the improvement of people's quality of life and lifestyle. There was a significant difference among different income households. (6) It is necessary to improve the availability of electricity further and to perfect electricity use policy in the less-developed regions.

Fuzzy logic based economical analysis of photovoltaic energy management

Since 2002 the European Union has seen a rapid growth in the photovoltaic (PV) sector. During the last two years incentives for PV installations were cut almost worldwide slowing the growth of the market. In this scenario the design of a new plant ensuring economic convenience is strongly related to household electricity consumption patterns and energy management actions. This paper presents a high-resolution model of domestic electricity use based on Fuzzy Logic Inference System. Taking into account consumers sensibility concerning the rational use of energy, the model gives as output a 1-min resolution overall electricity usage pattern of the household. The focus of this work is the use of a novel fuzzy model combined with a cost benefits analysis to evaluate the real economic benefits of load shifting actions. A case study is presented to quantify its effectiveness in the new net metering Italian scenario.

Fuzzy logic home energy consumption modeling for residential photovoltaic plant sizing in the new Italian scenario

In recent years, Italy has seen a rapid growth in the PV (photovoltaic) sector, following the introduction of the FIT (feed in tariff) scheme known as Conto Energia. In July 2013 the Italian government definitively cut FITs, leaving only tax benefits and a revised net metering scheme (known as “Scambio sul Posto”) for new PV installations. In this scenario, the design of a new PV plant ensuring savings on electricity bills is strongly related to household electricity consumption patterns. This paper presents a high-resolution model of domestic electricity use. The model is based on Fuzzy Logic Inference System. Using as inputs patterns of active occupancy and typical domestic habits, the fuzzy model give as output the likelihood to start each appliance within the next minute. The model has been validated with electricity demand data recorded over the period of one year within 12 dwellings in the central east coast of Italy. The tool has been used to evaluate the self consumption percentage to correctly size a residential photovoltaic plant in a case study. A cost benefits analysis is presented to show the effectiveness of PV-generation in the new Italian scenario.

Analysis of Household Electricity Consumption Patterns and Economy of Water Heating Shifting and Saving Bulbs

Analysis of Household Electricity Consumption Patterns and Economy of Water Heating Shifting and Saving BulbsThis article analyses household electricity consumption based on an object in Estonia. Energy consumption of workday and holiday by loads (including high and low tariff energy consumption) is discussed. The final part describes the evaluation of profitability of common investments of consumption shifting and replacing inefficient devices with more efficient ones. Additionally it describes shifting problems and shifting equipment profitability in real-time tariff system.

Determinants of Household Electricity Demand: A Two-Stage Probit Approach

Considerable interest on the part of policy makers in both the public and private sectors has spawned numerous studies of the determinants of residential electricity demand. As a result of increased attention given to the possibility of adjusting household electricity consumption patterns through price manipulation (as in the various peak-load pricing experiments), particular emphasis has been placed on obtaining an accurate estimate of the price elasticity of demand. Unfortunately, OLS applied to the conventional linear (or log-linear) demand specification is biased and inconsistent when the sample includes households that face declining-block rate schedules. The inconsistency results from the fact that, in the presence of declining-block rates, observed marginal price is a stochastic regressor that is negatively correlated with the regression error term. Some researchers have suggested that consistent OLS estimation can be achieved by merely respecifying the demand equation to include particular combinations of marginal price, average price, rate of decline of the price schedule, and real income corrected for intra-marginal price differences [1; 7; 8]. Others, realizing the futility of such an approach, have developed and applied instrumental variable (IV) techniques [4; 6; 11]. These IV methods, however, all involve an artificial linearization of the rate schedule which produces spurious negative correlation between the price variable and the random error term. As a result, such IV methods tend to overcompensate for actual correlation. Consequently, the IV estimated coefficients of the various demand determinants will exhibit biases opposite in sign to those of their OLS counterparts. For example, in the presence of declining-block rates, OLS tends to overestimate the price elasticity of demand (in absolute value), while IV methods produce elasticity estimates that are downward biased. A Two-Stage Probit (TSP) method developed by Terza and Welch [10] is not subject to the bias that plagues IV methods because TSP does not require a linear approximation to the rate schedule. Instead, the discrete and discontinuous nature of household responses to declining-block pricing is captured by incorporating an ordinal probit model that is based on a theory of consumer choice. In the present paper a linear electricity demand model is estimated using the TSP, OLS, and IV methods to demonstrate the effectiveness of the TSP method.