Table 1 Summary of selected articles reviewed
From: Forecasting household energy consumption based on lifestyle data using hybrid machine learning
Studies | Variables | Method(s) | Location (study) |
|---|---|---|---|
Nti et al | Socio-economic factors (age, income, family size, etc) | Artificial neural network (ANN) | Ghana |
zhang et al | Family pattern and aging society | Support vector machine | Japan |
Edward et al | Life schedules | Artificial neural network | China |
Grolinger et al | Electric gadgets | logit regression (LR) | Ireland |
Malatesta et al | Socio-economic factors | ARIMA | China |
Kwac et al | Occupant behavior | Linear regression | China |
Chou et al | Occupant behavior | multivariate linear regression (MLR) | Tokyo |
Alhussein et al | Income, temperature and population size | Regression model | China |
Li et al | Weather and family size | Markov model | Japan |
Almahamid et al | Calendar readings | Support vector machine (SVM) | Asia |