IoT Task Management Mechanism Based on Predictive Optimization for Efficient Energy Consumption in Smart Residential Buildings

2021 ◽  
pp. 111762
Author(s):  
Imran ◽  
Naeem Iqbal ◽  
Do Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Task Management ◽  
Management Mechanism ◽  
Efficient Energy

scholarly journals Analyzing Load Profiles of Energy Consumption to Infer Household Characteristics Using Smart Meters

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 773 ◽  
Author(s):  
Muhammad Fahim ◽  
Alberto Sillitti
Keyword(s):  
Energy Consumption ◽  
Time Series Data ◽  
Residential Buildings ◽  
Series Data ◽  
Smart Meters ◽  
Learning Mechanism ◽  
Efficient Energy ◽  
Household Characteristics ◽  
Univariate Time Series ◽  
Consumption Data

The increasing penetration of smart meters provides an excellent opportunity to monitor and analyze energy consumption in residential buildings. In this paper, we propose a framework to process the observed profiles of energy consumption to infer the household characteristics in residential buildings. Such characteristics can be used for improving resource allocation and for an efficient energy management that will ultimately contribute to reducing carbon dioxide (CO 2 ) emission. Our approach is based on automated extraction of features from univariate time-series data and development of a model through a variant of the decision trees technique (i.e., ensemble learning mechanism) random forest. We process and analyzed energy consumption data to answer four primitive questions. To evaluate the approach, we performed experiments on publicly available datasets. Our experiments show a precision of 82% and a recall of 81% in inferring household characteristics.

scholarly journals Efficient Energy Management System using Zigbee for Residential Application

2019 ◽  
Vol 8 (4) ◽  
pp. 1406-1411
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Management System ◽  
Residential Buildings ◽  
Peak Time ◽  
Energy Management System ◽  
Efficient Energy ◽  
Home Appliances ◽  
Cost Of Energy ◽  
The Cost

Energy management system is one of the challenging tasks associated with residential buildings. The cost of energy is purely based on the amount of energy consumed during peak hours. This paper focuses on an efficient energy management system for the control of energy consumption during peak hours. ZigBee module is used to monitor the energy consumed by the home appliances. The working of the proposed system is categorized into two modes of operation: normal time and peak time. During normal time, all home appliances can be operated and the cost of energy will be at normal rate. Whereas, during peak time, high rating machines will be shut down, that is controlled by ZigBee and the light loads will be operated from battery supply. Thus the proposed system reduces the energy consumption and is cost effective. Simulation analysis is done using proteus software. Hardware model is also implemented which proves that the proposed energy management system improves the energy efficiency.

Energy Consumption Pattern of Residential Buildings: Case Study of Residential Area in Batu Pahat, Johor

2020 ◽  
Vol 12 (9) ◽  
Author(s):  
Junaidah Jailani ◽  
◽  
Norsyalifa Mohamad ◽  
Muhammad Amirul Omar ◽  
Hauashdh Ali ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Residential Area ◽  
Consumption Pattern ◽  
Air Conditioners ◽  
Residential Sector ◽  
Waste Energy ◽  
Main Factor ◽  
Energy Consumption Pattern

According to the National Energy Balance report released by the Energy Commission of Malaysia in 2016, the residential sector uses 21.6% of the total energy in Malaysia. Residents waste energy through inefficient energy consumption and a lack of awareness. Building occupants are considered the main factor that influences energy consumption in buildings, and to change energy consumption on an overall scale, it is crucial to change individual behaviour. Therefore, this study focused on analysing the energy consumption pattern and the behaviour of consumers towards energy consumption in their homes in the residential area of Batu Pahat, Johor. A self-administrated questionnaire approach was employed in this study. The findings of this study showed that the excessive use of air conditioners was a significant factor in the increasing electricity bills of homeowners as well as the inefficient use of electrical appliances. Also, this study determined the effect of awareness on consumer behaviour. This study recommends ways to help minimise energy consumption in the residential area.

scholarly journals Ensemble Prediction Approach Based on Learning to Statistical Model for Efficient Building Energy Consumption Management

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 405
Author(s):  
Anam Nawaz Khan ◽  
Naeem Iqbal ◽  
Rashid Ahmad ◽  
Do-Hyeun Kim
Keyword(s):  
Energy Consumption ◽  
Statistical Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Experimental Results ◽  
Consumption Patterns ◽  
Ensemble Prediction ◽  
Short Term ◽  
Term Energy ◽  
Prediction Approach

With the development of modern power systems (smart grid), energy consumption prediction becomes an essential aspect of resource planning and operations. In the last few decades, industrial and commercial buildings have thoroughly been investigated for consumption patterns. However, due to the unavailability of data, the residential buildings could not get much attention. During the last few years, many solutions have been devised for predicting electric consumption; however, it remains a challenging task due to the dynamic nature of residential consumption patterns. Therefore, a more robust solution is required to improve the model performance and achieve a better prediction accuracy. This paper presents an ensemble approach based on learning to a statistical model to predict the short-term energy consumption of a multifamily residential building. Our proposed approach utilizes Long Short-Term Memory (LSTM) and Kalman Filter (KF) to build an ensemble prediction model to predict short term energy demands of multifamily residential buildings. The proposed approach uses real energy data acquired from the multifamily residential building, South Korea. Different statistical measures are used, such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R2 score, to evaluate the performance of the proposed approach and compare it with existing models. The experimental results reveal that the proposed approach predicts accurately and outperforms the existing models. Furthermore, a comparative analysis is performed to evaluate and compare the proposed model with conventional machine learning models. The experimental results show the effectiveness and significance of the proposed approach compared to existing energy prediction models. The proposed approach will support energy management to effectively plan and manage the energy supply and demands of multifamily residential buildings.

scholarly journals Towards Sustainable Energy Retrofitting, a Simulation for Potential Energy Use Reduction in Residential Buildings in Palestine

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3876
Author(s):  
Sameh Monna ◽  
Adel Juaidi ◽  
Ramez Abdallah ◽  
Aiman Albatayneh ◽  
Patrick Dutournie ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Use ◽  
Energy Savings ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Models ◽  
Water Heating ◽  
Heating And Cooling ◽  
Space Cooling ◽  
Cooling Loads

Since buildings are one of the major contributors to global warming, efforts should be intensified to make them more energy-efficient, particularly existing buildings. This research intends to analyze the energy savings from a suggested retrofitting program using energy simulation for typical existing residential buildings. For the assessment of the energy retrofitting program using computer simulation, the most commonly utilized residential building types were selected. The energy consumption of those selected residential buildings was assessed, and a baseline for evaluating energy retrofitting was established. Three levels of retrofitting programs were implemented. These levels were ordered by cost, with the first level being the least costly and the third level is the most expensive. The simulation models were created for two different types of buildings in three different climatic zones in Palestine. The findings suggest that water heating, space heating, space cooling, and electric lighting are the highest energy consumers in ordinary houses. Level one measures resulted in a 19–24 percent decrease in energy consumption due to reduced heating and cooling loads. The use of a combination of levels one and two resulted in a decrease of energy consumption for heating, cooling, and lighting by 50–57%. The use of the three levels resulted in a decrease of 71–80% in total energy usage for heating, cooling, lighting, water heating, and air conditioning.

Energy models for cost-optimal analysis: Development and calibration of residential reference building models for Northern Cyprus

2021 ◽  
pp. 1420326X2110130
Author(s):  
Manta Marcelinus Dakyen ◽  
Mustafa Dagbasi ◽  
Murat Özdenefe
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Mean Squared Error ◽  
Residential Buildings ◽  
Mean Bias Error ◽  
Bias Error ◽  
Sampled Data ◽  
Low Carbon ◽  
Building Stock ◽  
Northern Cyprus

Ambitious energy efficiency goals constitute an important roadmap towards attaining a low-carbon society. Thus, various building-related stakeholders have introduced regulations targeting the energy efficiency of buildings. However, some countries still lack such policies. This paper is an effort to help bridge this gap for Northern Cyprus, a country devoid of building energy regulations that still experiences electrical energy production and distribution challenges, principally by establishing reference residential buildings which can be the cornerstone for prospective building regulations. Statistical analysis of available building stock data was performed to determine existing residential reference buildings. Five residential reference buildings with distinct configurations that constituted over 75% floor area share of the sampled data emerged, with floor areas varying from 191 to 1006 m2. EnergyPlus models were developed and calibrated for five residential reference buildings against yearly measured electricity consumption. Values of Mean Bias Error (MBE) and Cumulative Variation of Root Mean Squared Error CV(RMSE) between the models’ energy consumption and real energy consumption on monthly based analysis varied within the following ranges: (MBE)monthly from –0.12% to 2.01% and CV(RMSE)monthly from 1.35% to 2.96%. Thermal energy required to maintain the models' setpoint temperatures for cooling and heating varied from 6,134 to 11,451 kWh/year.

scholarly journals Decarbonization of Residential Building Energy Supply: Impact of Cogeneration System Performance on Energy, Environment, and Economics

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2538
Author(s):  
Praveen K. Cheekatamarla
Keyword(s):  
Carbon Dioxide ◽  
Power Generation ◽  
Energy Consumption ◽  
Carbon Footprint ◽  
Residential Buildings ◽  
Building Energy ◽  
Primary Energy ◽  
Primary Energy Consumption ◽  
Cogeneration System ◽  
The Impact

Electrical and thermal loads of residential buildings present a unique opportunity for onsite power generation, and concomitant thermal energy generation, storage, and utilization, to decrease primary energy consumption and carbon dioxide intensity. This approach also improves resiliency and ability to address peak load burden effectively. Demand response programs and grid-interactive buildings are also essential to meet the energy needs of the 21st century while addressing climate impact. Given the significance of the scale of building energy consumption, this study investigates how cogeneration systems influence the primary energy consumption and carbon footprint in residential buildings. The impact of onsite power generation capacity, its electrical and thermal efficiency, and its cost, on total primary energy consumption, equivalent carbon dioxide emissions, operating expenditure, and, most importantly, thermal and electrical energy balance, is presented. The conditions at which a cogeneration approach loses its advantage as an energy efficient residential resource are identified as a function of electrical grid’s carbon footprint and primary energy efficiency. Compared to a heat pump heating system with a coefficient of performance (COP) of three, a 0.5 kW cogeneration system with 40% electrical efficiency is shown to lose its environmental benefit if the electrical grid’s carbon dioxide intensity falls below 0.4 kg CO2 per kWh electricity.

scholarly journals Modifying Building Energy-Saving Design Based on Field Research into Climate Features and Local Residents’ Habits

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 442
Author(s):  
Xiaoyue Zhu ◽  
Bo Gao ◽  
Xudong Yang ◽  
Zhong Yu ◽  
Ji Ni
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Residential Buildings ◽  
Average Energy ◽  
Field Tests ◽  
Building Energy ◽  
Building Energy Efficiency ◽  
Local Conditions ◽  
Building Energy Saving ◽  
Average Energy Consumption

In China, a surging urbanization highlights the significance of building energy conservation. However, most building energy-saving schemes are designed solely in compliance with prescriptive codes and lack consideration of the local situations, resulting in an unsatisfactory effect and a waste of funds. Moreover, the actual effect of the design has yet to be thoroughly verified through field tests. In this study, a method of modifying conventional building energy-saving design based on research into the local climate and residents’ living habits was proposed, and residential buildings in Panzhihua, China were selected for trial. Further, the modification scheme was implemented in an actual project with its effect verified by field tests. Research grasps the precise climate features of Panzhihua, which was previously not provided, and concludes that Panzhihua is a hot summer and warm winter zone. Accordingly, the original internal insulation was canceled, and the shading performance of the windows was strengthened instead. Test results suggest that the consequent change of SET* does not exceed 0.5 °C, whereas variations in the energy consumption depend on the room orientation. For rooms receiving less solar radiation, the average energy consumption increased by approximately 20%, whereas for rooms with a severe western exposure, the average energy consumption decreased by approximately 11%. On the other hand, the cost savings of removing the insulation layer are estimated at 177 million RMB (1 USD ≈ 6.5 RMB) per year. In conclusion, the research-based modification method proposed in this study can be an effective tool for improving building energy efficiency adapted to local conditions.

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