A predictive analysis of heart rates using machine learning techniques
Heart disease, caused by low heart rate, is one of the most significant causes of mortality in
the world today. Therefore, it is critical to monitor heart health by identifying the deviation in …
the world today. Therefore, it is critical to monitor heart health by identifying the deviation in …
Using long short-term memory networks to predict energy consumption of air-conditioning systems
C Zhou, Z Fang, X Xu, X Zhang, Y Ding… - Sustainable Cities and …, 2020 - Elsevier
The prediction of energy consumption is important for the efficient operation of building air-
conditioning systems. Most predicted models are based on historical energy consumption …
conditioning systems. Most predicted models are based on historical energy consumption …
Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system
IoT-enabled sensor devices and machine learning methods have played an essential role in
monitoring and forecasting mine hazards. In this paper, a prediction model has been …
monitoring and forecasting mine hazards. In this paper, a prediction model has been …
Toward smart lockdown: a novel approach for COVID-19 hotspots prediction using a deep hybrid neural network
COVID-19 caused the largest economic recession in the history by placing more than one
third of world's population in lockdown. The prolonged restrictions on economic and …
third of world's population in lockdown. The prolonged restrictions on economic and …
[HTML][HTML] Electricity demand forecasting with hybrid classical statistical and machine learning algorithms: Case study of Ukraine
TG Grandón, J Schwenzer, T Steens, J Breuing - Applied Energy, 2024 - Elsevier
This article presents a novel hybrid approach using classic statistics and machine learning
to forecast the national demand of electricity. As investment and operation of future energy …
to forecast the national demand of electricity. As investment and operation of future energy …
Time series analysis of electricity consumption forecasting using ARIMA model
Power consumption is a very important factor in smart grids for load management process.
Forecasting energy consumption is the first step in dealing with load management. For …
Forecasting energy consumption is the first step in dealing with load management. For …
A novel grey Lotka–Volterra model driven by the mechanism of competition and cooperation for energy consumption forecasting
Energy is the foundation for the stable operation and long-term growth of the national
economy. Quantifying the degree of competition and cooperation among different types of …
economy. Quantifying the degree of competition and cooperation among different types of …
Convolutional neural network-long short term memory optimization for accurate prediction of airflow in a ventilation system
AS Hati - Expert Systems With Applications, 2022 - Elsevier
Poor airflow ventilation systems fetch a progressively critical challenge for many working
areas, which transmits many calamitous physical consequences on operatives' health and …
areas, which transmits many calamitous physical consequences on operatives' health and …
Limitations of machine learning for building energy prediction: ASHRAE Great Energy Predictor III Kaggle competition error analysis
Research is needed to explore the limitations and potential for improvement of machine
learning for building energy prediction. With this aim, the ASHRAE Great Energy Predictor III …
learning for building energy prediction. With this aim, the ASHRAE Great Energy Predictor III …
DECODE: Data-driven energy consumption prediction leveraging historical data and environmental factors in buildings
Energy prediction in buildings plays a crucial role in effective energy management. Precise
predictions are essential for achieving optimal energy consumption and distribution within …
predictions are essential for achieving optimal energy consumption and distribution within …