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 …
A deep learning framework for building energy consumption forecast
Increasing global building energy demand, with the related economic and environmental
impact, upsurges the need for the design of reliable energy demand forecast models. This …
impact, upsurges the need for the design of reliable energy demand forecast models. This …
Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads
Z Zhang, WC Hong - Knowledge-Based Systems, 2021 - Elsevier
Accurate electric load forecasting is critical in guaranteeing the efficiency of the load
dispatch and supply by a power system, which prevents the wasting of electricity and …
dispatch and supply by a power system, which prevents the wasting of electricity and …
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
Long short-term memory network-based metaheuristic for effective electric energy consumption prediction
The Electric Energy Consumption Prediction (EECP) is a complex and important process in
an intelligent energy management system and its importance has been increasing rapidly …
an intelligent energy management system and its importance has been increasing rapidly …
Machine learning driven smart electric power systems: Current trends and new perspectives
MS Ibrahim, W Dong, Q Yang - Applied Energy, 2020 - Elsevier
The current power systems are undergoing a rapid transition towards their more active,
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
flexible, and intelligent counterpart smart grid, which brings about tremendous challenges in …
A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India
Selecting a suitable energy demand forecasting method is challenging due to the complex
interplay of long-term trends, short-term seasonalities, and uncertainties. This paper …
interplay of long-term trends, short-term seasonalities, and uncertainties. This paper …
State-of-the-art artificial intelligence techniques for distributed smart grids: A review
SS Ali, BJ Choi - Electronics, 2020 - mdpi.com
The power system worldwide is going through a revolutionary transformation due to the
integration with various distributed components, including advanced metering infrastructure …
integration with various distributed components, including advanced metering infrastructure …
Data analytics in the supply chain management: Review of machine learning applications in demand forecasting
A Aamer, LP Eka Yani… - Operations and Supply …, 2020 - journal.oscm-forum.org
In today's fast-paced global economy coupled with the availability of mobile internet and
social networks, several business models have been disrupted. This disruption brings a …
social networks, several business models have been disrupted. This disruption brings a …
Prediction of energy consumption and evaluation of affecting factors in a full-scale WWTP using a machine learning approach
Abstract Treatment of municipal wastewater to meet the stringent effluent quality standards is
an energy-intensive process and the main contributor to the costs of wastewater treatment …
an energy-intensive process and the main contributor to the costs of wastewater treatment …