Record Details

<p>Stacking Machine Learning Models to Forecast Hourly and Daily Electricity Consumption of Household Using Internet of Things</p>

Online Publishing @ NISCAIR

View Archive Info
 
 
Field Value
 
Authentication Code dc
 
Title Statement <p>Stacking Machine Learning Models to Forecast Hourly and Daily Electricity Consumption of Household Using Internet of Things</p>
 
Added Entry - Uncontrolled Name Banga, Alisha ; Electronics and Computer Discipline, IIT Roorkee, Saharanpur Campus, Saharanpur, UP, India 247 001
Ahuja, Ravinder ; Electronics and Computer Discipline, IIT Roorkee, Saharanpur Campus, Saharanpur, UP, India 247 001
Sharma, S C; Electronics and Computer Discipline, IIT Roorkee, Saharanpur Campus, Saharanpur, UP, India 247 001
 
Uncontrolled Index Term Electricity Demand, Ensemble Learning, Regression, Smart Meter, Time Series Forecasting
 
Summary, etc. <p>The objective of this paper is to design an efficient electricity consumption forecasting model using stacking ensemble technique and Internet of Things (IoT). Two stage process is applied in this paper. In the first stage, fifteen forecasting models (Auto-ARIMA, Holt-Winter (Additive), Exponential, Facebook Prophet, Light Gradient Boosting, AdaBoost, Support Vector Regression, Decision Tree, Extra Tree, Random Forest, Elastic net, K-Nearest Neighbour’s, XGBoost, Linear Regression, Long Short Term Memory) are applied to forecast electricity consumption at an hourly and daily level. In the next stage, the best four models are selected and stacked. We have considered the dataset of energy consumption by electrical appliances per minute in a house over seven days. The models are evaluated using root mean square error (RMSE), mean absolute error (MAE), R-square, and mean absolute percentage error (MAPE). The results show that the extra tree performed better among all the algorithms, and stacking further improves performance. Elastic net and decision tree algorithms have taken less time as compared to other models applied in this study.</p>
 
Publication, Distribution, Etc. Journal of Scientific and Industrial Research (JSIR)
2021-11-29 17:02:53
 
Electronic Location and Access application/pdf
http://op.niscair.res.in/index.php/JSIR/article/view/42241
 
Data Source Entry Journal of Scientific and Industrial Research (JSIR); ##issue.vol## 80, ##issue.no## 10 (2021): Journal of Scientific and Industrial Research
 
Language Note en