Record Details

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

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Title Stacking Machine Learning Models to Forecast Hourly and Daily Electricity Consumption of Household Using Internet of Things
 
Creator Banga, Alisha
Ahuja, Ravinder
Sharma, S C
 
Subject Electricity Demand
Regression
Smart Meter
Ensemble Learning
Smart Meter
Time Series Forecasting
 
Description 894-904
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.
 
Date 2021-10-05T11:45:35Z
2021-10-05T11:45:35Z
2021-10
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58228
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(10) [October 2021]