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[HTML][HTML] Multivariate time-series forecasting: A review of deep learning methods in internet of things applications to smart cities
Smart cities are urban areas that utilize digital solutions to enhance the efficiency of
conventional networks and services for sustainable growth, optimized resource …
conventional networks and services for sustainable growth, optimized resource …
Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting
Due to global warming and climate changes, buildings including residential and commercial
are significant contributors to energy consumption. To this end, net zero energy building …
are significant contributors to energy consumption. To this end, net zero energy building …
Dual stream network with attention mechanism for photovoltaic power forecasting
The operations of renewable power generation systems highly depend on precise
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
Photovoltaic (PV) power forecasting, providing significant economic, and environmental …
[HTML][HTML] Energy consumption forecasting for the digital-twin model of the building
The aim of the paper is to propose a new approach to forecast the energy consumption for
the next day using the unique data obtained from a digital twin model of a building. In the …
the next day using the unique data obtained from a digital twin model of a building. In the …
Predicting energy consumption using stacked LSTM snapshot ensemble
The ability to make accurate energy predictions while considering all related energy factors
allows production plants, regulatory bodies, and governments to meet energy demand and …
allows production plants, regulatory bodies, and governments to meet energy demand and …
[HTML][HTML] Deep character-level anomaly detection based on a convolutional autoencoder for zero-day phishing URL detection
Considering the fatality of phishing attacks, the data-driven approach using massive URL
observations has been verified, especially in the field of cyber security. On the other hand …
observations has been verified, especially in the field of cyber security. On the other hand …
Harnessing AI for solar energy: Emergence of transformer models
This review emphasizes the critical need for accurate integration of solar energy into power
grids. It meticulously examines the advancements in transformer models for solar …
grids. It meticulously examines the advancements in transformer models for solar …
[HTML][HTML] Bayesian optimization algorithm-based statistical and machine learning approaches for forecasting short-term electricity demand
This article focuses on develo** both statistical and machine learning approaches for
forecasting hourly electricity demand in Ontario. The novelties of this study include (i) …
forecasting hourly electricity demand in Ontario. The novelties of this study include (i) …
A novel short-term residential electric load forecasting method based on adaptive load aggregation and deep learning algorithms
T Hou, R Fang, J Tang, G Ge, D Yang, J Liu, W Zhang - Energies, 2021 - mdpi.com
Short-term residential load forecasting is the precondition of the day-ahead and intra-day
scheduling strategy of the household microgrid. Existing short-term electric load forecasting …
scheduling strategy of the household microgrid. Existing short-term electric load forecasting …
Advancing ensemble learning techniques for residential building electricity consumption forecasting: Insight from explainable artificial intelligence
Accurate electricity consumption forecasting in residential buildings has a direct impact on
energy efficiency and cost management, making it a critical component of sustainable …
energy efficiency and cost management, making it a critical component of sustainable …