Learning Approach for Energy Consumption Forecasting in Residential Microgrid

Vikash Kumar Saini; Ravindra Singh; Dinesh Kumar Mahto; Rajesh Kumar; Akhilesh Mathur

doi:10.1109/KPEC54747.2022.9814744

Learning Approach for Energy Consumption Forecasting in Residential Microgrid

Vikash Kumar Saini
, Ravindra Singh
, Dinesh Kumar Mahto
, Rajesh Kumar
, Akhilesh Mathur

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

19 Citations (Scopus)

Abstract

Residential energy consumption plays an important role in the social and economic development of the country. Highly accurate forecasting can aid in decision making and forecast for future residential electricity demand for smooth management of power system operations. However, residential load characteristics are influenced by human behavior, seasonal variation, and other social factors. Thus the share of uncertainty in the load will be at a significant level. Therefore, obtaining highly accurate load forecasts is a challenging task for the power system operator. In this research article, the authors propose a recurrent neural network based LSTM, GRU, Bi-LSTM, and Bi-GRU based learning approach for short-term residential energy consumption forecasting. Simulation results on a real 30 minute time interval energy consumption data set for 9 months of a residential prosumer microgrid located in central-Norway. The numerical results are show that the Bi-GRU model is achieving higher performance than others on the given load data set.

Original language	English
Title of host publication	2022 IEEE Kansas Power and Energy Conference, KPEC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665465915
DOIs	https://doi.org/10.1109/KPEC54747.2022.9814744
Publication status	Published - 2022
Externally published	Yes
Event	3rd IEEE Kansas Power and Energy Conference, KPEC 2022 - Manhattan, United States Duration: 25 Apr 2022 → 26 Apr 2022

Publication series

Name	2022 IEEE Kansas Power and Energy Conference, KPEC 2022

Conference

Conference	3rd IEEE Kansas Power and Energy Conference, KPEC 2022
Country/Territory	United States
City	Manhattan
Period	25/04/22 → 26/04/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 8 Decent Work and Economic Growth

Keywords

deep learning algorithms
load forecasting
Residential grid

ASJC Scopus subject areas

Safety, Risk, Reliability and Quality
Artificial Intelligence
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Electrical and Electronic Engineering

Access to Document

10.1109/KPEC54747.2022.9814744

Cite this

Saini, V. K., Singh, R., Mahto, D. K., Kumar, R., & Mathur, A. (2022). Learning Approach for Energy Consumption Forecasting in Residential Microgrid. In 2022 IEEE Kansas Power and Energy Conference, KPEC 2022 (2022 IEEE Kansas Power and Energy Conference, KPEC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/KPEC54747.2022.9814744

@inproceedings{df04fc6cd6fc444caba9575b0e374a39,

title = "Learning Approach for Energy Consumption Forecasting in Residential Microgrid",

abstract = "Residential energy consumption plays an important role in the social and economic development of the country. Highly accurate forecasting can aid in decision making and forecast for future residential electricity demand for smooth management of power system operations. However, residential load characteristics are influenced by human behavior, seasonal variation, and other social factors. Thus the share of uncertainty in the load will be at a significant level. Therefore, obtaining highly accurate load forecasts is a challenging task for the power system operator. In this research article, the authors propose a recurrent neural network based LSTM, GRU, Bi-LSTM, and Bi-GRU based learning approach for short-term residential energy consumption forecasting. Simulation results on a real 30 minute time interval energy consumption data set for 9 months of a residential prosumer microgrid located in central-Norway. The numerical results are show that the Bi-GRU model is achieving higher performance than others on the given load data set.",

keywords = "deep learning algorithms, load forecasting, Residential grid",

author = "Saini, \{Vikash Kumar\} and Ravindra Singh and Mahto, \{Dinesh Kumar\} and Rajesh Kumar and Akhilesh Mathur",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 3rd IEEE Kansas Power and Energy Conference, KPEC 2022 ; Conference date: 25-04-2022 Through 26-04-2022",

year = "2022",

doi = "10.1109/KPEC54747.2022.9814744",

language = "English",

series = "2022 IEEE Kansas Power and Energy Conference, KPEC 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2022 IEEE Kansas Power and Energy Conference, KPEC 2022",

address = "United States",

}

Saini, VK, Singh, R, Mahto, DK, Kumar, R & Mathur, A 2022, Learning Approach for Energy Consumption Forecasting in Residential Microgrid. in 2022 IEEE Kansas Power and Energy Conference, KPEC 2022. 2022 IEEE Kansas Power and Energy Conference, KPEC 2022, Institute of Electrical and Electronics Engineers Inc., 3rd IEEE Kansas Power and Energy Conference, KPEC 2022, Manhattan, United States, 25/04/22. https://doi.org/10.1109/KPEC54747.2022.9814744

Learning Approach for Energy Consumption Forecasting in Residential Microgrid. / Saini, Vikash Kumar; Singh, Ravindra; Mahto, Dinesh Kumar et al.
2022 IEEE Kansas Power and Energy Conference, KPEC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (2022 IEEE Kansas Power and Energy Conference, KPEC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning Approach for Energy Consumption Forecasting in Residential Microgrid

AU - Saini, Vikash Kumar

AU - Singh, Ravindra

AU - Mahto, Dinesh Kumar

AU - Kumar, Rajesh

AU - Mathur, Akhilesh

PY - 2022

Y1 - 2022

N2 - Residential energy consumption plays an important role in the social and economic development of the country. Highly accurate forecasting can aid in decision making and forecast for future residential electricity demand for smooth management of power system operations. However, residential load characteristics are influenced by human behavior, seasonal variation, and other social factors. Thus the share of uncertainty in the load will be at a significant level. Therefore, obtaining highly accurate load forecasts is a challenging task for the power system operator. In this research article, the authors propose a recurrent neural network based LSTM, GRU, Bi-LSTM, and Bi-GRU based learning approach for short-term residential energy consumption forecasting. Simulation results on a real 30 minute time interval energy consumption data set for 9 months of a residential prosumer microgrid located in central-Norway. The numerical results are show that the Bi-GRU model is achieving higher performance than others on the given load data set.

AB - Residential energy consumption plays an important role in the social and economic development of the country. Highly accurate forecasting can aid in decision making and forecast for future residential electricity demand for smooth management of power system operations. However, residential load characteristics are influenced by human behavior, seasonal variation, and other social factors. Thus the share of uncertainty in the load will be at a significant level. Therefore, obtaining highly accurate load forecasts is a challenging task for the power system operator. In this research article, the authors propose a recurrent neural network based LSTM, GRU, Bi-LSTM, and Bi-GRU based learning approach for short-term residential energy consumption forecasting. Simulation results on a real 30 minute time interval energy consumption data set for 9 months of a residential prosumer microgrid located in central-Norway. The numerical results are show that the Bi-GRU model is achieving higher performance than others on the given load data set.

KW - deep learning algorithms

KW - load forecasting

KW - Residential grid

UR - https://www.scopus.com/pages/publications/85135118651

U2 - 10.1109/KPEC54747.2022.9814744

DO - 10.1109/KPEC54747.2022.9814744

M3 - Conference contribution

AN - SCOPUS:85135118651

T3 - 2022 IEEE Kansas Power and Energy Conference, KPEC 2022

BT - 2022 IEEE Kansas Power and Energy Conference, KPEC 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE Kansas Power and Energy Conference, KPEC 2022

Y2 - 25 April 2022 through 26 April 2022

ER -

Learning Approach for Energy Consumption Forecasting in Residential Microgrid

Abstract

Publication series

Conference

UN SDGs

Keywords

ASJC Scopus subject areas

Access to Document

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