Predictive Analytics of Energy Usage by IoT-Based Smart Home Appliances for Green Urban Development

2022 ◽  
Vol 22 (2) ◽  
pp. 1-26
Author(s):  
Mohammad Shorfuzzaman ◽  
M. Shamim Hossain
Keyword(s):  
Energy Consumption ◽  
Urban Development ◽  
Predictive Models ◽  
Energy Demand ◽  
Smart Home ◽  
Predictive Analytics ◽  
Moving Average ◽  
Energy Usage ◽  
Total Energy Consumption ◽  
Home Appliances

Green IoT primarily focuses on increasing IoT sustainability by reducing the large amount of energy required by IoT devices. Whether increasing the efficiency of these devices or conserving energy, predictive analytics is the cornerstone for creating value and insight from large IoT data. This work aims at providing predictive models driven by data collected from various sensors to model the energy usage of appliances in an IoT-based smart home environment. Specifically, we address the prediction problem from two perspectives. Firstly, an overall energy consumption model is developed using both linear and non-linear regression techniques to identify the most relevant features in predicting the energy consumption of appliances. The performances of the proposed models are assessed using a publicly available dataset comprising historical measurements from various humidity and temperature sensors, along with total energy consumption data from appliances in an IoT-based smart home setup. The prediction results comparison show that LSTM regression outperforms other linear and ensemble regression models by showing high variability ( R 2 ) with the training (96.2%) and test (96.1%) data for selected features. Secondly, we develop a multi-step time-series model using the auto regressive integrated moving average (ARIMA) technique to effectively forecast future energy consumption based on past energy usage history. Overall, the proposed predictive models will enable consumers to minimize the energy usage of home appliances and the energy providers to better plan and forecast future energy demand to facilitate green urban development.

scholarly journals Use of mechanical resonance in impact machines

2018 ◽  
Vol 211 ◽  
pp. 17006
Author(s):  
Wieslaw Fiebig ◽  
Jakub Wrobel
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Demand ◽  
Drive System ◽  
Mechanical Resonance ◽  
Total Energy Consumption ◽  
At Resonance ◽  
Innovative Method ◽  
Drive Systems

An innovative method exploiting mechanical resonance in machines drive systems, especially useful in impact machines, has been developed. Accumulation of energy at resonance can be applied to the drive system in a similar way as flywheels in eccentric presses. Under resonance conditions, the total energy consumption of the oscillating mass is equal to the energy lost due the damping forces. Energy accumulated in the oscillator can be several times greater than the energy supplied continuously to the oscillator. The developed method can be used in many applications, especially in impacting machines. Finally, the energy demand of resonance punching press will be compared with the energy demand of eccentric press.

scholarly journals Energy Use Efficiency Past-to-Future Evaluation: An International Comparison

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3804 ◽  
Author(s):  
Chia-Nan Wang ◽  
Thi-Duong Nguyen ◽  
Min-Chun Yu
Keyword(s):  
United States ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Demand ◽  
Energy Use ◽  
The United States ◽  
Total Energy Consumption ◽  
The Future ◽  
The Status ◽  
Energy Efficiency Evaluation

Despite the many benefits that energy consumption brings to the economy, consuming energy also leads nations to expend more resources on environmental pollution. Therefore, energy efficiency has been proposed as a solution to improve national economic competitiveness and sustainability. However, the growth in energy demand is accelerating while policy efforts to boost energy efficiency are slowing. To solve this problem, the efficiency gains in countries where energy consumption efficiency is of the greatest concern such as China, India, the United States, and Europe, especially, emerging economies, is central. Additionally, governments must take greater policy actions. Therefore, this paper studied 25 countries from Asia, the Americas, and Europe to develop a method combining the grey method (GM) and data envelopment analysis (DEA) slack-based measure model (SMB) to measure and forecast the energy efficiency, so that detailed energy efficiency evaluation can be made from the past to the future; moreover, this method can be extended to more countries around the world. The results of this study reveal that European countries have a higher energy efficiency than countries in Americas (except the United States) and Asian countries. Our findings also show that an excess of total energy consumption is the main reason causing the energy inefficiency in most countries. This study contributes to policymaking and strategy makers by sharing the understanding of the status of energy efficiency and providing insights for the future.

The Relationship between Economics Growth and Energy Consumption in China-A Empirical Analysis Based on Energy Kuznets Curve

2014 ◽  
Vol 1073-1076 ◽  
pp. 2457-2461
Author(s):  
Chang Sheng Li ◽  
Qing Ling Li ◽  
Zhong Min Lei ◽  
Han Yang ◽  
Hui Qing Qu
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Empirical Analysis ◽  
Energy Demand ◽  
Kuznets Curve ◽  
Total Energy Consumption ◽  
Energy Demands ◽  
High Level ◽  
The Relationship ◽  
Relative High Level

These paper investigated the relationship between economics development and energy demands based on Energy Kuznets Curve (EFC) in China. The results show that, the prospects of economics and energy demand in China in further will undergo three important stages to 2050.The peak of energy demand maybe around 2035 and the corresponding total energy demand maybe amount 5.7 billion tce. In 2035, the GDP per capital maybe about 17000 (2005 US$) and the urbanization will reach a relative high level. It is urgent for China to take actions to curb the increasing total energy consumption.

scholarly journals Power analysis of smart home appliances based on SSA-TCN

2021 ◽  
Vol 2078 (1) ◽  
pp. 012060
Author(s):  
Chao Tang ◽  
Yong Tang ◽  
Huihui Liang ◽  
Linghao Zhang ◽  
Siyu Xiang
Keyword(s):  
Energy Consumption ◽  
Power Analysis ◽  
Smart Home ◽  
Singular Spectrum Analysis ◽  
Recognition Rate ◽  
Monitoring Methods ◽  
Convolutional Network ◽  
Home Appliances ◽  
Operation And Maintenance ◽  
Smart Home Appliances

Abstract The popularity of smart home equipment has led to higher requirements for equipment automation operation and maintenance. However, the energy consumption status and hidden faults of household equipment cannot be controlled in time only by using traditional monitoring methods. Therefore, this paper proposes a methods of power analysis for smart home appliances based on SSA-TCN using the energy consumption data of smart home appliances. The effective information of the data is extracted through the SSA singular spectrum analysis method, and the data sequence is input into the sequential convolutional network for judgment, so that the energy consumption status and working status of the equipment is obtained. The actual data is used as the training set and the test set to verify the recognition rate of the model. The experimental results show that the recognition rate of the method is about 82%, which provides an effective way for equipment automation and intelligent operation and maintenance.

scholarly journals Energy consumption of a passenger car with a hybrid powertrain in real traffic conditions

2021 ◽  
Author(s):  
Andrzej Bieniek ◽  
Mariusz Graba ◽  
Jarosław Mamala ◽  
Krzysztof Prażnowski ◽  
Krystian Hennek
Keyword(s):  
Energy Consumption ◽  
Energy Expenditure ◽  
Total Energy ◽  
Energy Demand ◽  
Combustion Engine ◽  
Operating Conditions ◽  
Drive System ◽  
Hybrid Drive ◽  
Total Energy Consumption ◽  
Passenger Car

The analysis of energy consumption in a hybrid drive system of a passenger car in real road conditions is an important factor determining its operational indicators. The article presents energy consumption analysis of a car equipped with an advanced Plug-in Hybrid Drive System (PHEV), driving in real road conditions on a test section of about 51 km covered in various environmental conditions and seasons. Particular attention was paid to the energy consumption resulting from the cooperation of two independent drive units, analyzed in terms of the total energy expenditure. The energy consumption obtained from fuel and energy collected from the car’s batteries for each run over the total distance of 12,500 km was summarized. The instantaneous values of energy consumption for the hybrid drive per kilometer of distance traveled in car’s real operating conditions range from 0.6 to 1.4 MJ/km, with lower values relating to the vehicle operation only with electric drive. The upper range applies to the internal combustion engine, which increases not only the energy expenditure in the TTW (Tank-to-Wheel) system, but also CO2 emissions to the environment. Based on the experimental data, the curves of total energy consumption per kilometer of the road section traveled were determined, showing a close correlation with the actual operating conditions. Obtained values were compared with homologation data from the WLTP test of the tested passenger car, where the average value of energy demand is 1.1 MJ/km and the CO2 emission is 23 g/km.

scholarly journals EVALUATION OF ENERGY-EFFICIENT RETROFIT POTENTIAL FOR GOVERNMENT OFFICES IN INDIA

2021 ◽  
Vol 6 (2) ◽  
pp. 03-17
Author(s):  
Gazal Dandia ◽  
◽  
Pratheek Sudhakaran ◽  
Chaitali Basu ◽  
◽  
...  
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Efficient ◽  
Energy Demand ◽  
Energy Savings ◽  
High Energy ◽  
Payback Period ◽  
Total Energy Consumption ◽  
The Cost

Introduction: High energy consumption by buildings is a great threat to the environment and one of the major causes of climate change. With a population of 1.4 billion people and one of the fastest-growing economies in the world, India is extremely vital for the future of global energy markets. The energy demand for construction activities continues to rise and it is responsible for over one-third of global final energy consumption. Currently, buildings in India account for 35% of total energy consumption and the value is growing by 8% annually. Around 11% of total energy consumption are attributed to the commercial sector. Energy-efficient retrofitting of the built environments created in recent decades is a pressing urban challenge. Presently, most energy-efficient retrofit projects focus mainly on the engineering aspects. In this paper, we evaluate various retrofitting options, such as passive architectural interventions, active technological interventions, or a combination of both, to create the optimum result for the selected building. Methods: Based on a literature study and case examples, we identified various energy-efficient retrofit measures, and then examined and evaluated those as applied to the case study of Awas Bhawan (Rajasthan Housing Board Headquarters), Jaipur, India. For the evaluation, we developed a simulation model using EQuest for each energy measure and calculated the resultant energy savings. Then, based on the cost of implementation and the cost of energy saved, we calculated the payback period. Finally, an optimum retrofit solution was formulated with account for the payback period and ease of installation. Results and discussion: The detailed analysis of various energy-efficient retrofit measures as applied to the case study indicates that the most feasible options for retrofit resulting in optimum energy savings with short payback periods include passive architecture measures and equipment upgrades.

scholarly journals Energy and Emission Analysis of Residential Sector: A Case Study for Reshunga Municipality in Nepal

2018 ◽  
Vol 4 ◽  
pp. 17-27
Author(s):  
Biswambhar Panthi ◽  
Nawraj Bhattarai
Keyword(s):  
Energy Consumption ◽  
Energy Demand ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Total Energy Consumption ◽  
Emission Analysis ◽  
Hilly Region ◽  
Ghg Reduction ◽  
Final Energy Demand

This paper presents energy consumption in a municipality within hilly region and also analyzes GHG emission under different scenario. For the purpose of study Reshunga municipality was taken, situated in Gulmi district of Nepal occupying an area of 82.74 sq.km. For collection of data, 368 houses were surveyed and the locals were interviewed on their annual consumption. The total energy consumption was 214.8 TJ where 78.25% was supplied by wood. LPG shared 16.14% of demand. Cooking (58%) and water boiling (26%) were the most demanding task. Most of the houses were equipped with ICS, with share 55% of energy demand in cooking. Four different scenario were studied viz. BAU, DSM, BSP and SDG. In, BAU scenario, the energy consumption will reach 245.3 TJ. In DSM scenario and BSP scenario the final energy demand will reduce to 230.7 TJ and 216.2 TJ. In SDG scenario, energy demand is reduced by 23.14%. The share of LPG increases to 22.36 % and electricity demand becomes more than doubles from reaching 10.64% in SDG. From year 2017-2030, there will be total accumulative increase of electricity requirement by 47.4 TJ, whereas total cumulative decrease of 433.5 TJ equivalents can be resulted in consumption of wood in SDG scenario. Cost-Benefit analysis study revealed that DSM will require an investment of 43.03K US$ for demand technologies and will reduce emission by 8.69 tCO2e. DSM will be cheapest in terms of cost per GHG reduction. SDG will cost 645.46K US$ and results in reduction of GHG by 47.79K tCO2e.

Energy Consumption Forecasting in Hong Kong Using ARIMA and Artificial Neural Networks Models

2014 ◽  
Vol 672-674 ◽  
pp. 2085-2097 ◽  
Author(s):  
Sue Ling Lai ◽  
Ming Liu ◽  
Kuo Cheng Kuo ◽  
Ray Chang
Keyword(s):  
Hong Kong ◽  
Energy Consumption ◽  
Energy Demand ◽  
Moving Average ◽  
Critical Role ◽  
Forecast Accuracy ◽  
Demand Forecast ◽  
Forecast Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

There have been considerable efforts contributed to the development of effective energy demand forecast models due to its critical role for economic development and environmental protection. This study focused on the adoption of artificial neural network (ANN) and autoregressive integrated moving average (ARIMA) models for energy consumption forecasting in Hong Kong over the period of 1975-2010. Four predictors were considered, including population, GDP, exports, and total visitor arrivals. The results show most ANN models demonstrate acceptable forecast accuracy when single predictor is considered. The best single input model is the case with GDP as predictor. Population and exports are the next proper single inputs. The model with total visitor arrivals as sole predictor does not perform satisfactorily. This indicates that tourism development demonstrates a different pattern from that of energy consumption. In addition, the forecast accuracy of ANN does not improve considerably as the number of predictors increase. Findings imply that with the ANN approach, choosing appropriate predictors is more important than increasing the number of predictors. On the other hand, ARIMA generates forecasts as accurate as some good cases by ANN. Results suggest that ARIMA is not only a parsimonious but effective approach for energy consumption forecasting in Hong Kong.

scholarly journals Intelligent Optimization Mechanism Based on an Objective Function for Efficient Home Appliances Control in an Embedded Edge Platform

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1460
Author(s):  
Rongxu Xu ◽  
Wenquan Jin ◽  
Yonggeun Hong ◽  
Do-Hyeun Kim
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Energy Consumption ◽  
Objective Function ◽  
Smart Home ◽  
Edge Computing ◽  
Rule Based ◽  
Home Appliances ◽  
Iot Devices ◽  
Optimization Mechanism

In recent years the ever-expanding internet of things (IoT) is becoming more empowered to revolutionize our world with the advent of cutting-edge features and intelligence in an IoT ecosystem. Thanks to the development of the IoT, researchers have devoted themselves to technologies that convert a conventional home into an intelligent occupants-aware place to manage electric resources with autonomous devices to deal with excess energy consumption and providing a comfortable living environment. There are studies to supplement the innate shortcomings of the IoT and improve intelligence by using cloud computing and machine learning. However, the machine learning-based autonomous control devices lack flexibility, and cloud computing is challenging with latency and security. In this paper, we propose a rule-based optimization mechanism on an embedded edge platform to provide dynamic home appliance control and advanced intelligence in a smart home. To provide actional control ability, we design and developed a rule-based objective function in the EdgeX edge computing platform to control the temperature states of the smart home. Compared to cloud computing, edge computing can provide faster response and higher quality of services. The edge computing paradigm provides better analysis, processing, and storage abilities to the data generated from the IoT sensors to enhance the capability of IoT devices concerning computing, storage, and network resources. In order to satisfy the paradigm of distributed edge computing, all the services are implemented as microservices. The microservices are connected to each other through REST APIs based on the constrained IoT devices to provide all the functionalities that accomplish a trade-off between energy consumption and occupant-desired environment setting for the smart home appliances. We simulated our proposed system to control the temperature of a smart home; through experimental findings, we investigated the application against the delay time and overall memory consumption by the embedded edge system of EdgeX. The result of this research work suggests that the implemented services operated efficiently in the raspberry pi 3 hardware of IoT devices.

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