IoT-Based Smart Energy Management and Load Shifting for Residential Consumption Optimization

The increasing energy demand and rising electricity costs have intensified the need for efficient energy management in residential buildings. Load imbalances in power grids and cost surges during peak demand periods are pushing consumers toward more strategic and conscious consumption models. This study focuses on optimizing household electricity consumption through load shifting strategies. To achieve this, a low-cost, Internet of Things (IoT)-based Smart Energy Management System (SEMS) was developed and implemented in a real household. Its performance was then compared with a commercial reference device, revealing an impressive accuracy rate of 99.98%. Electricity consumption data recorded over a one-year period using SEMS was analyzed to assess voltage fluctuations, frequency variations, and energy usage patterns. Daily, weekly, and seasonal consumption trends were identified. Moreover, classifying shiftable and non-shiftable loads enabled a comprehensive evaluation of the load-shifting potential. Scenario-based analyses demonstrated that shifting 25%, 50%, and 75% of shiftable loads to lower-cost tariff periods could result in 9.8%, 17.6%, and 26.1% cost savings, respectively. These findings indicate that SEMS enhances energy efficiency and reduces electricity costs. With its user-friendly interface and low installation cost, SEMS is well-suited for widespread adoption and presents an effective solution for time-based load shifting. Additionally, by encouraging users to adopt more efficient consumption habits, SEMS is expected to contribute to sustainable energy management.

Authors

Institutions

Publication Details

Journal
Celal Bayar Üniversitesi Fen Bilimleri Dergisi
Published
2025-09-26
DOI
https://doi.org/10.18466/cbayarfbe.1660181
Primary Topic
Smart Grid Energy Management
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

IoT-Based Smart Energy Management and Load Shifting for Residential Consumption Optimization

Mehmet Taştan
Celal Bayar Üniversitesi Fen Bilimleri Dergisi
Smart Grid Energy Management
article

IoT-Based Smart Energy Management and Load Shifting for Residential Consumption Optimization

Mehmet Taştan
article en

Abstract

The increasing energy demand and rising electricity costs have intensified the need for efficient energy management in residential buildings. Load imbalances in power grids and cost surges during peak demand periods are pushing consumers toward more strategic and conscious consumption models. This study focuses on optimizing household electricity consumption through load shifting strategies. To achieve this, a low-cost, Internet of Things (IoT)-based Smart Energy Management System (SEMS) was developed and implemented in a real household. Its performance was then compared with a commercial reference device, revealing an impressive accuracy rate of 99.98%. Electricity consumption data recorded over a one-year period using SEMS was analyzed to assess voltage fluctuations, frequency variations, and energy usage patterns. Daily, weekly, and seasonal consumption trends were identified. Moreover, classifying shiftable and non-shiftable loads enabled a comprehensive evaluation of the load-shifting potential. Scenario-based analyses demonstrated that shifting 25%, 50%, and 75% of shiftable loads to lower-cost tariff periods could result in 9.8%, 17.6%, and 26.1% cost savings, respectively. These findings indicate that SEMS enhances energy efficiency and reduces electricity costs. With its user-friendly interface and low installation cost, SEMS is well-suited for widespread adoption and presents an effective solution for time-based load shifting. Additionally, by encouraging users to adopt more efficient consumption habits, SEMS is expected to contribute to sustainable energy management.

Celal Bayar Üniversitesi Fen Bilimleri DergisiVol. 21(3)
Manisa Celal Bayar University (TR)
Openalex Percentile: Top 74%
Smart Grid Energy Management
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.