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 …
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better generalization
performance. Currently, deep learning architectures are showing better performance …
performance. Currently, deep learning architectures are showing better performance …
Effective energy consumption forecasting using empirical wavelet transform and long short-term memory
L Peng, L Wang, D Xia, Q Gao - energy, 2022 - Elsevier
Energy consumption is an important issue of global concern. Accurate energy consumption
forecasting can help balance energy demand and energy production. Although there are …
forecasting can help balance energy demand and energy production. Although there are …
[HTML][HTML] Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN
Electrical load forecasting plays a vital role in the operation and planning of power plants for
the utility companies and policy makers to design stable and reliable energy infrastructure …
the utility companies and policy makers to design stable and reliable energy infrastructure …
Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid
G Hafeez, KS Alimgeer, I Khan - Applied Energy, 2020 - Elsevier
Accurate electric load forecasting is important due to its application in the decision making
and operation of the power grid. However, the electric load profile is a complex signal due to …
and operation of the power grid. However, the electric load profile is a complex signal due to …
Load forecasting techniques for power system: Research challenges and survey
The main and pivot part of electric companies is the load forecasting. Decision-makers and
think tank of power sectors should forecast the future need of electricity with large accuracy …
think tank of power sectors should forecast the future need of electricity with large accuracy …
Forecasting methods in energy planning models
KB Debnath, M Mourshed - Renewable and Sustainable Energy Reviews, 2018 - Elsevier
Energy planning models (EPMs) play an indispensable role in policy formulation and energy
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
sector development. The forecasting of energy demand and supply is at the heart of an EPM …
Load demand forecasting of residential buildings using a deep learning model
L Wen, K Zhou, S Yang - Electric Power Systems Research, 2020 - Elsevier
In smart grid and smart building environment, it is important to implement accurate load
demand forecasting of residential buildings. This plays an important role in supporting the …
demand forecasting of residential buildings. This plays an important role in supporting the …
Deep belief network based electricity load forecasting: An analysis of Macedonian case
A number of recent studies use deep belief networks (DBN) with a great success in various
applications such as image classification and speech recognition. In this paper, a DBN …
applications such as image classification and speech recognition. In this paper, a DBN …
Joint bagged-boosted artificial neural networks: Using ensemble machine learning to improve short-term electricity load forecasting
This paper uses artificial neural networks (ANNs) based ensemble machine learning for
improving short-term electricity load forecasting. Unlike existing methods, the proposed …
improving short-term electricity load forecasting. Unlike existing methods, the proposed …