Intelligent Forecasting and Optimization in Electrical Power Systems

© 2022 by the authors; CC BY-NC-ND license

hybrid AC/DC microgrid; optimization of configuration and operating states; CLONALG; modified hypermutation operator; wind energy; wind farm; ensemble methods; short-term forecasting; electric energy production; machine learning; deep neural network; swarm intelligence; voltage control; voltage quality; renewable energy; metaheuristic optimisation; medium voltage; Q(U) characteristics; microgrids; operation control; power generation; PV system; very-short-term forecasting; machine learning; interval type-2 fuzzy logic system; distribution of electric power; distributed storage and generation; smart grids; power distribution reliability; information and communication technology; energy efficiency; cooling towers; chillers; evolutionary multi-objective optimization; mid-term forecast; e-mobility; electric vehicles (EVs); power system demand; load profile forecast; machine learning (ML); electricity load forecasting; bootstrap aggregating; singular spectrum analysis; time series forecasting; calendar variation; short-term forecasting; electrical power demand; power systems; autoregressive forecasting methods; classical forecasting methods; artificial intelligence methods; Big Data; machine learning; Data Mining; auto-regressive integrated moving average (ARIMA); long short-term memory (LSTM); Optuna; isolation forest (IF); elliptic envelope (EE); one-class support vector machine (OCSVM); neuromorphic computing; spiking neural network; short-term wind power forecasting; random forest; regression tree; pattern representation of time series; short-term load forecasting; transfer learning; wind power; photovolatic power; autoencoders; deep learning; time series; wind power prediction; machine learning; deep learning; hybrid methods; time series analysis; forecasting error; evaluation criteria metrics; wind power forecasting; wind turbine; wind farm; statistical analysis of errors; hybrid methods; ensemble methods; machine learning; deep neural network; medium-term load forecasting; pattern-based forecasting; time-series preprocessing; photovoltaic (PV); forecast; behind-the-meter (BTM); spatio-temporal; strategic training; deep neural networks; LSTM; time series prediction; optimisation; GA; PSO; n/a