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Sustainability in Oman: Energy Consumption Forecasting using R
Objective: Smart city projects are still in their initial research stages in Oman. This paper aims to prove the effectiveness of smart cities by using Data Mining Techniques (DMT) to predict energy consumption in Oman. Methods: Data collected from thirteen residential and eight industrial meters are used for electricity consumption forecast. Detailed data analysis is carried out using K-means clustering and time-series forecasting in R. Energy consumption data is modeled using average, naive, seasonal naive, Seasonal decomposition of Time Series by Loess (STL) +Random Walk with Drift (RWD), Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal component (TBATS) and Autoregressive Integrated Moving Average (ARIMA) models. Findings: Even though the dataset isn’t characterized by seasons or trends, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error measures suggest that electricity consumption for residential sector is more accurately forecasted using TBATS model. Energy consumption for small, medium and large scale industries, on the other hand are more accurately predicted by TBATS, Average and STL + RWD models respectively. Applications: The obtained results confirm the efficiency in forecasting energy consumption in Oman using time series models in order to initiate smart city implementation.
Data Mining, Energy Consumption, Smart City, Clustering, Time-Series Forecasting, R.
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