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A novel temporal feature selection based LSTM model for electrical short-term load forecasting
An accurate electrical Short-term Load Forecasting (STLF) is an eminent factor in the power
generation, electrical load dispatching and energy planning for the power supply …
generation, electrical load dispatching and energy planning for the power supply …
Enhancing PV hosting capacity and mitigating congestion in distribution networks with deep learning based PV forecasting and battery management
The extensive deployment of domestic photovoltaic (PV) systems may result in exceeding
the limits of the network's PV hosting capacity (HC), which leads to energy delivery …
the limits of the network's PV hosting capacity (HC), which leads to energy delivery …
Comparative analysis of machine learning techniques for non-intrusive load monitoring
Non-intrusive load monitoring (NILM) has emerged as a pivotal technology in energy
management applications by enabling precise monitoring of individual appliance energy …
management applications by enabling precise monitoring of individual appliance energy …
Techno-economic analysis and energy forecasting study of domestic and commercial photovoltaic system installations in Estonia
The Baltic countries have good potential for solar photovoltaic (PV) energy generation, as on
average 15 hours of sunlight is available in summer. Another potential option is to …
average 15 hours of sunlight is available in summer. Another potential option is to …
[HTML][HTML] Exploratory data analysis based short-term electrical load forecasting: A comprehensive analysis
Power system planning in numerous electric utilities merely relies on the conventional
statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is …
statistical methodologies, such as ARIMA for short-term electrical load forecasting, which is …
[HTML][HTML] A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
The rapid growth of power electronics-based devices, such as electric vehicles and
renewable energy systems, has introduced nonlinear components into power systems …
renewable energy systems, has introduced nonlinear components into power systems …
A novel attention-based long short term memory and fully connected neutral network approach for production energy consumption prediction under complex working …
Continuously growing demands due to the predictable faults or abnormal events of the
flexible production line are becoming a challenge to easily cause the energy waste, which …
flexible production line are becoming a challenge to easily cause the energy waste, which …
Machine learning and deep learning techniques for residential load forecasting: A comparative analysis
Load forecasting has become a very important parameter in modem power systems. These
smart power systems require flexibility, smooth operation, scalability, and better demand …
smart power systems require flexibility, smooth operation, scalability, and better demand …
XgBoost based short-term electrical load forecasting considering trends & periodicity in historical data
The effective planning and management of residential electricity demand requires precise
forecasting of the short-term electrical load. A novel approach is proposed for short-term …
forecasting of the short-term electrical load. A novel approach is proposed for short-term …
Short-term residental DC load forecasting using extreme gradient boost (XgBoost) algorithm
Accurate forecasts of short-term electricity consumption are essential for efficient energy
management in buildings and residential households. This research introduces a new …
management in buildings and residential households. This research introduces a new …