COMPARATIVE ANALYSIS OF VARIOUS FORECAST MODELS OF ELECTRICITY CONSUMPTION IN SMART BUILDINGS
DOI:
https://doi.org/10.37943/21AJSD8750Keywords:
smart building, energy consumption forecasting, optimization algorithms, machine learning, computational processesAbstract
The rapidly growing field of smart building technology depends heavily on accurate electricity consumption forecasting. By anticipating energy demands, building managers can optimize resource allocation, minimize waste, and enhance overall efficiency. This study provides a comprehensive comparative analysis of various models used to forecast electricity consumption in smart buildings, highlighting their strengths, limitations, and suitability for different use cases. The investigation focuses on three major categories of forecasting models: statistical methods, machine learning techniques, and hybrid approaches. Statistical models, such as the Moving Average Method, leverage historical data patterns to predict future trends. These models enable analysts to utilize predictive analytics, simulating real-world environments and helping them make more informed decisions. The study offers a detailed comparison of several predictive models applied to Internet of Things (IoT) data, with a particular emphasis on energy consumption in smart buildings. Among the short-term forecasting models examined are gradient-enhanced regressors (XGBoost), random forest (RF), and long short-term memory networks (LSTM). The performance of these models was evaluated based on prediction errors to identify the most accurate one. Time series, machine learning, and hybrid models used to predict energy consumption are considered and analyzed. The focus is on the accuracy of forecasts and their applicability in real-world conditions, taking into account factors such as climate change and data obtained from Internet of Things (IoT) sensors. The analysis shows that hybrid models combining machine learning and time series provide the best prediction accuracy over different time horizons. It also highlights the importance of integrating user behavior data and using IoT technologies to improve model accuracy. The results can be applied to create energy-efficient control systems in smart buildings and optimize energy consumption.
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