Forecasting Residential Monthly Electricity Consumption using Smart Meter Data

With the rapid development and wide application of distributed generation technology and new energy trading methods, the integrated energy system has developed rapidly in Europe in recent years and has become the focus of new strategic competition and cooperation among countries. As a key technology and decision-making approach for operation, optimization, and control of integrated energy systems, power consumption prediction faces new challenges. The user-side power demand and load characteristics change due to the influence of distributed energy. At the same time, in the open retail market of electricity sales, the forecast of electricity consumption faces the power demand of small-scale users, which is more easily disturbed by random factors than by a traditional load forecast. Therefore, this study proposes a model based on X12 and Seasonal and Trend decomposition using Loess (STL) decomposition of monthly electricity consumption forecasting methods. The first use of the STL model according to the properties of electricity each month is its power consumption time series decomposition individuation. It influences the factorization of monthly electricity consumption into season, trend, and random components. Then, the change in the characteristics of the three components over time is considered. Finally, the appropriate model is selected to predict the components in the reconfiguration of the monthly electricity consumption forecast. A forecasting program is developed based on R language and MATLAB, and a case study is conducted on the power consumption data of a university campus containing distributed energy. Results show that the proposed method is reasonable and effective.

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Electricity Consumption Clustering Using Smart Meter Data

Energies ◽

10.3390/en11040859 ◽

2018 ◽

Vol 11 (4) ◽

pp. 859 ◽

Cited By ~ 19

Author(s):

Alexander Tureczek ◽

Per Nielsen ◽

Henrik Madsen

Keyword(s):

Electricity Consumption ◽

Smart Meter

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Forecasting Electricity Consumption Using Fuzzy Time Series

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.30.22305 ◽

2018 ◽

Vol 7 (4.30) ◽

pp. 342

Author(s):

K.G. Tay ◽

Y.Y. Choy ◽

C.C. Chew

Keyword(s):

Time Series ◽

Membership Function ◽

Electricity Consumption ◽

Fuzzy Time Series ◽

Infrastructure Development ◽

Power Load ◽

Increasing Demand ◽

Facility Expansion ◽

Electric Power Load ◽

Monthly Electricity Consumption

Electricity consumption forecasting is important for effective operation, planning and facility expansion of power system. Accurate forecasts can save operating and maintenance costs, increased the reliability of power supply and delivery system, and correct decisions for future development. There is a great development of Universiti Tun Hussein Onn Malaysia (UTHM) infrastructure since its formation in 1993. The development will be accompanied with the increasing demand of electricity. Hence, there is a need to forecast the UTHM electricity consumption for future decisions on generating electric power, load switching, and infrastructure development. Therefore, in this study, the Fuzzy time series (FTS) with trapezoidal membership function was implemented on the UTHM monthly electricity consumption from January 2011 to December 2017 to forecast January to December 2018 monthly electricity consumption. The procedure of the FTS and trapezoidal membership function was described together with January data. FTS is able to forecast UTHM electricity consumption quite well.

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A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Energies ◽

10.3390/en10101446 ◽

2017 ◽

Vol 10 (10) ◽

pp. 1446 ◽

Cited By ~ 12

Author(s):

Fateh Melzi ◽

Allou Same ◽

Mohamed Zayani ◽

Latifa Oukhellou

Keyword(s):

Mixture Model ◽

Electricity Consumption ◽

Smart Meter

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Design of electricity tariff plans using gap statistic for K-means clustering based on consumers monthly electricity consumption data

International Journal of Energy Sector Management ◽

10.1108/ijesm-02-2016-0005 ◽

2017 ◽

Vol 11 (2) ◽

pp. 295-310 ◽

Cited By ~ 1

Author(s):

Ravindra R. Rathod ◽

Rahul Dev Garg

Keyword(s):

Design Method ◽

Electricity Consumption ◽

Consumption Pattern ◽

Content Type ◽

Gap Statistic ◽

Use Of Data ◽

Daily Data ◽

Consumption Data ◽

Practical Implications ◽

Monthly Electricity Consumption

Purpose Electricity consumption around the world and in India is continuously increasing over the years. Presently, there is a huge diversity in electricity tariffs across states in India. This paper aims to focus on development of new tariff design method using K-means clustering and gap statistic. Design/methodology/approach Numbers of tariff plans are selected using gap-statistic for K-means clustering and regression analysis is used to deduce new tariffs from existing tariffs. The study has been carried on nearly 27,000 residential consumers from Sangli city, Maharashtra State, India. Findings These tariff plans are proposed with two objectives: first, possibility to shift consumer’s from existing to lower tariff plan for saving electricity and, second, to increase revenue by increasing tariff charges using Pay-by-Use policy. Research limitations/implications The study can be performed on hourly or daily data using automatic meter reading and to introduce Time of Use or demand based tariff. Practical implications The proposed study focuses on use of data mining techniques for tariff planning based on consumer’s electricity usage pattern. It will be helpful to detect abnormalities in consumption pattern as well as forecasting electricity usage. Social implications Consumers will be able to decide own monthly electricity consumption and related tariff leading to electricity savings, as well as high electricity consumption consumers have to pay more tariff charges for extra electricity usage. Originality/value To remove the disparity in various tariff plans across states and country, proposed method will help to provide a platform for designing uniform tariff for entire country based on consumer’s electricity consumption data.

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Smart metering system data analytics platform using multicore edge computing

International Journal of Reconfigurable and Embedded Systems (IJRES) ◽

10.11591/ijres.v10.i1.pp11-17 ◽

2021 ◽

Vol 10 (1) ◽

pp. 11

Author(s):

Juan C. Olivares-Rojas ◽

Enrique Reyes-Archundia ◽

José A. Gutiérrez-Gnecchi ◽

Ismael Molina-Moreno ◽

Adriana C. Téllez-Anguiano ◽

...

Keyword(s):

Internet Of Things ◽

Smart Grid ◽

Data Processing ◽

Data Analytics ◽

Electricity Consumption ◽

Edge Computing ◽

Smart Meter ◽

Smart Metering ◽

Smart Meters ◽

System Data

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

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