Distributed energy resources and the application of AI, IoT, and blockchain in smart grids
Smart grid (SG), an evolving concept in the modern power infrastructure, enables the two-
way flow of electricity and data between the peers within the electricity system networks …
way flow of electricity and data between the peers within the electricity system networks …
A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings
MQ Raza, A Khosravi - Renewable and Sustainable Energy Reviews, 2015 - Elsevier
Electrical load forecasting plays a vital role in order to achieve the concept of next
generation power system such as smart grid, efficient energy management and better power …
generation power system such as smart grid, efficient energy management and better power …
Forecasting energy demand, wind generation and carbon dioxide emissions in Ireland using evolutionary neural networks
The ability to accurately predict future power demands, power available from renewable
resources and the environmental impact of power generation is vital to the energy sector for …
resources and the environmental impact of power generation is vital to the energy sector for …
Everything is image: CNN-based short-term electrical load forecasting for smart grid
Electrical load forecasting is of great significance to guarantee the system stability under
large disturbances, and optimize the distribution of energy resources in the smart grid …
large disturbances, and optimize the distribution of energy resources in the smart grid …
Multivariate ensemble forecast framework for demand prediction of anomalous days
An accurate load forecast is always important for the power industry and energy players as it
enables stakeholders to make critical decisions. In addition, its importance is further …
enables stakeholders to make critical decisions. In addition, its importance is further …
A cross-disciplinary comparison of multimodal data fusion approaches and applications: Accelerating learning through trans-disciplinary information sharing
Multimodal data fusion (MMDF) is the process of combining disparate data streams (of
different dimensionality, resolution, type, etc.) to generate information in a form that is more …
different dimensionality, resolution, type, etc.) to generate information in a form that is more …
A machine learning model ensemble for mixed power load forecasting across multiple time horizons
The increasing penetration of renewable energy sources tends to redirect the power
systems community's interest from the traditional power grid model towards the smart grid …
systems community's interest from the traditional power grid model towards the smart grid …
Review of Short‐Term Load Forecasting for Smart Grids Using Deep Neural Networks and Metaheuristic Methods
Forecasting electricity load demand is critical for power system planning and energy
management. In particular, accurate short‐term load forecasting (STLF), which focuses on …
management. In particular, accurate short‐term load forecasting (STLF), which focuses on …
Generalized regression neural network for long-term electricity load forecasting
The availability of electricity demand is very high. Many households and industrial
equipment are using electricity as the source energy. The reliability of the power system in …
equipment are using electricity as the source energy. The reliability of the power system in …
Meta-learning in multivariate load demand forecasting with exogenous meta-features
Although many studies have examined various types of single load demand prediction
algorithms, it is yet a challenging decision to select the best predictor. Geographical …
algorithms, it is yet a challenging decision to select the best predictor. Geographical …