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 **a, 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
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 …