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Title	Smart Street Lighting With Prediction Algorithm
ID_Doc	51461
Authors	Singh G.; Hanafi E.
Year	2022
Published	2022 IEEE 20th Student Conference on Research and Development, SCOReD 2022
DOI	http://dx.doi.org/10.1109/SCOReD57082.2022.9974139
Abstract	Power load forecasting, which is an important part of power system planning, is the foundation of power system economic operation. It is essential for power system planning and operation. Electricity consumption has increased at an exponential rate over the last few decades, putting strain on electrical distributors. Among the tasks that companies can undertake is to understand their customers' behavior to adjust their electricity consumption patterns and to have a planned supply chain adaptation. This allows forecasting future energy consumption based on the consumption history and other variables of the users themselves. This paper proposes the best deep learning model for forecasting electricity consumption for streetlights for a duration of one week and one year ahead by evaluating certain performance criteria such as Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) for each model. The selection of the proposed model involves three main stages namely shortlisting the deep learning models, evaluating the RMSE and MAPE scores, and comparing each models' score. A prototype based smart streetlight system was developed to obtain the ground truth value of power consumption value of the Light Emitting Diode. Then, this value was then transformed to daily power consumption (kWh) in winter and other seasons based on Northern Hemisphere region. From the findings, it was observed that the Long Short-Term Memory (LSTM) performs the most accurate forecasting by obtaining the lowest RMSE and MAPE score which made the model the most preferred choice. © 2022 IEEE.
Author Keywords	Deep Learning; power consumption; prediction algorithm; smart cities; smart street light system

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