High electricity demand causes large amounts of energy to be wasted, indicating that many people are unaware of their energy consumption. This also results in higher energy costs in electricity bills. Tenaga Nasional Berhad (TNB), which is the largest electricity producer in Malaysia has resolved 85% of the more than 18,000 "sky-high" electricity bill complaints received from consumers, especially in April and May 2019. Therefore, accurate energy demand forecasts are crucial for users to understand their electricity consumption. This paper presents the method of load forecasting with analysis that has been done with different machine learning techniques namely Long Term Memory (LSTM) and Autoregression (AR). Both of these methods are used to study and predict energy consumption patterns. Accordingly, this study aims to examine whether and how newly developed deep learning algorithms such as Long Short Term Memory (LSTM) are better than traditional algorithms such as the Autoregression (AR) model. The model’s prediction performance was assessed using the Mean Absolute Percentage Error (MAPE) and R-square (R) regression score. Based on the analysis performed, LSTM was found to provide more accurate predictions than AR, especially for large-scale data sets. Therefore, the LSTM model was chosen as the forecast model used for the second key feature proposed in this project, which is to convert the energy consumption rate into currency value. Such conversions are made based on standard tariffs and trigger warnings to users to help them realize their energy consumption is more wasteful compared to their normal energy consumption patterns.
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In-Text Citation: (Azman et al., 2021)
To Cite this Article: Azman, M. S., Zaini, N., & Latip, M. F. A. (2021). Computation of Energy in Currency Values Using Long Short-term Memory-based Approach for Energy Prediction. International Journal of Academic Research in Business and Social Sciences, 11(9), 489–501.
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