Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

Predicting Smart Office Electricity Consumption in Response to Weather Conditions Using Deep Learning

smart office;electricity consumption prediction;weather for load forecasting;deep learning;time series

This study investigates the intricate relationship between electricity consumption in smart office environments, temporal elements such as time, and external factors such asweather conditions. Usinga data set that encompasseselectrical consumption statistics, temporal data, and weather conditions, the research employs preprocessing, visualization, and featureengineering techniques. The predictive model for electric energy usage is constructed using deep learning architectures, including Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional Gated Recurrent Unit (Bi-GRU). Evaluation metrics reveal that the LSTM model outperforms others, achieving minimal Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). The study acknowledges the limitations of the data set,particularly whencomparing electricity usage during workhours and outside working hours in a residential context. Future research aims to address these limitations, considering detailed meteorological data, missing data imputation, and real-time applications for broaderapplicability. The ultimate goal is to develop a predictive model that serves as a valuable tool for improvingenergy management in smart office settings, optimizing electricity usage, and contributing to long-term firm profitability

Zikri Wahyuzi1, Ahmad Luthfi2, Dhomas Hatta Fudholi3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5530/897

Magister Informatika, Informatika, Universitas Islam Indonesia, Yogyakarta, Indonesia

18-02-2024

FAJAR BAGUS W

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