AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
In theory, building automation and management systems (BAMSs) can provide all the
components and functionalities required for analyzing and operating buildings. However, in …
components and functionalities required for analyzing and operating buildings. However, in …
Electrical load forecasting models for different generation modalities: a review
The intelligent management of power in electrical utilities depends on the high significance
of load forecasting models. Since the industries are digitalized, power generation is …
of load forecasting models. Since the industries are digitalized, power generation is …
An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting †
Recently, multistep-ahead prediction has attracted much attention in electric load forecasting
because it can deal with sudden changes in power consumption caused by various events …
because it can deal with sudden changes in power consumption caused by various events …
A CNN-Assisted deep echo state network using multiple Time-Scale dynamic learning reservoirs for generating Short-Term solar energy forecasting
M Ishaq, S Kwon - Sustainable Energy Technologies and Assessments, 2022 - Elsevier
The integration of renewable energy generation presented an important development
around the globe and conveys countless financial, commercial, and environmental …
around the globe and conveys countless financial, commercial, and environmental …
[HTML][HTML] Toward explainable electrical load forecasting of buildings: A comparative study of tree-based ensemble methods with Shapley values
Electrical load forecasting of buildings is crucial in designing an energy operation strategy
for smart city realization. Although artificial intelligence techniques have demonstrated …
for smart city realization. Although artificial intelligence techniques have demonstrated …
Conditional tabular GAN-based two-stage data generation scheme for short-term load forecasting
Load forecasting is one of the critical tasks for enhancing the energy efficiency of smart
grids. Even though recent deep learning-based load forecasting models have shown …
grids. Even though recent deep learning-based load forecasting models have shown …
Monthly electric load forecasting using transfer learning for smart cities
Monthly electric load forecasting is essential to efficiently operate urban power grids.
Although diverse forecasting models based on artificial intelligence techniques have been …
Although diverse forecasting models based on artificial intelligence techniques have been …
Robust building energy consumption forecasting using an online learning approach with R ranger
Recently, the online learning-based stacking ensemble approach has yielded satisfactory
short-term load forecasting (STLF) because it can effectively reflect recent building energy …
short-term load forecasting (STLF) because it can effectively reflect recent building energy …
A new random forest algorithm based on learning automata
M Savargiv, B Masoumi… - Computational …, 2021 - Wiley Online Library
The goal of aggregating the base classifiers is to achieve an aggregated classifier that has a
higher resolution than individual classifiers. Random forest is one of the types of ensemble …
higher resolution than individual classifiers. Random forest is one of the types of ensemble …
Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building
Electric energy consumption forecasting is an interesting, challenging, and important issue
in energy management and equipment efficiency improvement. Existing approaches are …
in energy management and equipment efficiency improvement. Existing approaches are …