Listing 1 - 7 of 7 |
Sort by
|
Choose an application
This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.
Research & information: general --- Physics --- Prophet model --- Holt–Winters model --- long-term forecasting --- peak load --- prophet model --- multiple seasonality --- time series --- demand --- load --- forecast --- DIMS --- irregular --- galvanizing --- short-term electrical load forecasting --- machine learning --- deep learning --- statistical analysis --- parameters tuning --- CNN --- LSTM --- short-term load forecast --- Artificial Neural Network --- deep neural network --- recurrent neural network --- attention --- encoder decoder --- online training --- bidirectional long short-term memory --- multi-layer stacked --- neural network --- short-term load forecasting --- power system --- Prophet model --- Holt–Winters model --- long-term forecasting --- peak load --- prophet model --- multiple seasonality --- time series --- demand --- load --- forecast --- DIMS --- irregular --- galvanizing --- short-term electrical load forecasting --- machine learning --- deep learning --- statistical analysis --- parameters tuning --- CNN --- LSTM --- short-term load forecast --- Artificial Neural Network --- deep neural network --- recurrent neural network --- attention --- encoder decoder --- online training --- bidirectional long short-term memory --- multi-layer stacked --- neural network --- short-term load forecasting --- power system
Choose an application
This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.
Research & information: general --- Physics --- Prophet model --- Holt–Winters model --- long-term forecasting --- peak load --- prophet model --- multiple seasonality --- time series --- demand --- load --- forecast --- DIMS --- irregular --- galvanizing --- short-term electrical load forecasting --- machine learning --- deep learning --- statistical analysis --- parameters tuning --- CNN --- LSTM --- short-term load forecast --- Artificial Neural Network --- deep neural network --- recurrent neural network --- attention --- encoder decoder --- online training --- bidirectional long short-term memory --- multi-layer stacked --- neural network --- short-term load forecasting --- power system
Choose an application
This reprint introduces advanced prediction models focused on power load forecasting. Models based on artificial intelligence and more traditional approaches are shown, demonstrating the real possibilities of use to improve prediction in this field. Models of LSTM neural networks, LSTM networks with a SESDA architecture, in even LSTM-CNN are used. On the other hand, multiple seasonal Holt-Winters models with discrete seasonality and the application of the Prophet method to demand forecasting are presented. These models are applied in different circumstances and show highly positive results. This reprint is intended for both researchers related to energy management and those related to forecasting, especially power load.
Prophet model --- Holt–Winters model --- long-term forecasting --- peak load --- prophet model --- multiple seasonality --- time series --- demand --- load --- forecast --- DIMS --- irregular --- galvanizing --- short-term electrical load forecasting --- machine learning --- deep learning --- statistical analysis --- parameters tuning --- CNN --- LSTM --- short-term load forecast --- Artificial Neural Network --- deep neural network --- recurrent neural network --- attention --- encoder decoder --- online training --- bidirectional long short-term memory --- multi-layer stacked --- neural network --- short-term load forecasting --- power system
Choose an application
Challenging problems arise in all segments of energy industries—generation, transmission, distribution and consumption. Optimization models and methods play a key role in offering decision/policy makers better information to assist them in making sounder decisions at different levels, ranging from operational to strategic planning.
mixed integer linear programming --- fuzzy set theory --- stochastic programming --- mixed integer linear programing --- variable renewable power --- generation efficiency --- optimization --- flexibility option --- portfolio analysis --- firefighting --- semi-mean-absolute deviation model --- component outage --- energy network --- predicted mean vote (PMV) --- generation expansion planning --- building microgrid --- demand side management --- stochastic robust optimization --- oil storage plants --- long-term forecasting --- multi-criteria decision making (MCDM) --- life cycle cost --- graph theory --- scenario-based multistage stochastic programming --- optimal power generation mix --- heating ventilation and air-conditioning (HVAC) --- intermittent sources --- electric-power structure adjustment --- technique for the order of preference by similarity to the ideal solution (TOPSIS) --- integrated energy system --- Markov chain Monte Carlo --- nondominated sorting genetic algorithm (NSGA) --- domino effect --- energy system management model --- electrical distribution systems --- microgrid operation --- influence diagram --- net demand --- wind power forecasting --- energy conservation and emissions reduction --- feasible operation region --- meshed topology --- occupancy-based control --- islanded microgrids --- combined heat and power --- multi-objective optimization --- re-optimization and rescheduling
Choose an application
Artificial intelligence techniques, such as expert systems, fuzzy logic, and artificial neural network techniques have become efficient tools in modeling and control applications. For example, there are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality. So, this book presents different applications of artificial intelligence techniques in advanced high-tech electronics, such as applications in power electronics, motor drives, renewable energy systems and smart grids.
droop curve --- frequency regulation --- fuzzy logic --- the rate of change of frequency --- reserve power --- smart grid --- energy Internet --- convolutional neural network --- decision optimization --- deep reinforcement learning --- electric load forecasting --- non-dominated sorting genetic algorithm II --- multi-layer perceptron --- adaptive neuro-fuzzy inference system --- meta-heuristic algorithms --- automatic generation control --- fuzzy neural network control --- thermostatically controlled loads --- back propagation algorithm --- particle swarm optimization --- load disaggregation --- artificial intelligence --- cognitive meters --- machine learning --- state machine --- NILM --- non-technical losses --- semi-supervised learning --- knowledge embed --- deep learning --- distribution network equipment --- condition assessment --- multi information source --- fuzzy iteration --- current balancing algorithm --- level-shifted SPWM --- medium-voltage applications --- multilevel current source inverter --- motor drives --- phase-shifted carrier SPWM --- STATCOM --- electricity forecasting --- CNN–LSTM --- very short-term forecasting (VSTF) --- short-term forecasting (STF) --- medium-term forecasting (MTF) --- long-term forecasting (LTF) --- asynchronous motor --- linear active disturbance rejection control --- error differentiation --- vector control --- renewable energy --- solar power plant --- Data Envelopment Analysis (DEA) --- Fuzzy Analytical Network Process (FANP) --- Fuzzy Theory
Choose an application
Artificial intelligence techniques, such as expert systems, fuzzy logic, and artificial neural network techniques have become efficient tools in modeling and control applications. For example, there are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality. So, this book presents different applications of artificial intelligence techniques in advanced high-tech electronics, such as applications in power electronics, motor drives, renewable energy systems and smart grids.
History of engineering & technology --- droop curve --- frequency regulation --- fuzzy logic --- the rate of change of frequency --- reserve power --- smart grid --- energy Internet --- convolutional neural network --- decision optimization --- deep reinforcement learning --- electric load forecasting --- non-dominated sorting genetic algorithm II --- multi-layer perceptron --- adaptive neuro-fuzzy inference system --- meta-heuristic algorithms --- automatic generation control --- fuzzy neural network control --- thermostatically controlled loads --- back propagation algorithm --- particle swarm optimization --- load disaggregation --- artificial intelligence --- cognitive meters --- machine learning --- state machine --- NILM --- non-technical losses --- semi-supervised learning --- knowledge embed --- deep learning --- distribution network equipment --- condition assessment --- multi information source --- fuzzy iteration --- current balancing algorithm --- level-shifted SPWM --- medium-voltage applications --- multilevel current source inverter --- motor drives --- phase-shifted carrier SPWM --- STATCOM --- electricity forecasting --- CNN–LSTM --- very short-term forecasting (VSTF) --- short-term forecasting (STF) --- medium-term forecasting (MTF) --- long-term forecasting (LTF) --- asynchronous motor --- linear active disturbance rejection control --- error differentiation --- vector control --- renewable energy --- solar power plant --- Data Envelopment Analysis (DEA) --- Fuzzy Analytical Network Process (FANP) --- Fuzzy Theory --- droop curve --- frequency regulation --- fuzzy logic --- the rate of change of frequency --- reserve power --- smart grid --- energy Internet --- convolutional neural network --- decision optimization --- deep reinforcement learning --- electric load forecasting --- non-dominated sorting genetic algorithm II --- multi-layer perceptron --- adaptive neuro-fuzzy inference system --- meta-heuristic algorithms --- automatic generation control --- fuzzy neural network control --- thermostatically controlled loads --- back propagation algorithm --- particle swarm optimization --- load disaggregation --- artificial intelligence --- cognitive meters --- machine learning --- state machine --- NILM --- non-technical losses --- semi-supervised learning --- knowledge embed --- deep learning --- distribution network equipment --- condition assessment --- multi information source --- fuzzy iteration --- current balancing algorithm --- level-shifted SPWM --- medium-voltage applications --- multilevel current source inverter --- motor drives --- phase-shifted carrier SPWM --- STATCOM --- electricity forecasting --- CNN–LSTM --- very short-term forecasting (VSTF) --- short-term forecasting (STF) --- medium-term forecasting (MTF) --- long-term forecasting (LTF) --- asynchronous motor --- linear active disturbance rejection control --- error differentiation --- vector control --- renewable energy --- solar power plant --- Data Envelopment Analysis (DEA) --- Fuzzy Analytical Network Process (FANP) --- Fuzzy Theory
Choose an application
Artificial intelligence techniques, such as expert systems, fuzzy logic, and artificial neural network techniques have become efficient tools in modeling and control applications. For example, there are several benefits in optimizing cost-effectiveness, because fuzzy logic is a methodology for the handling of inexact, imprecise, qualitative, fuzzy, and verbal information systematically and rigorously. A neuro-fuzzy controller generates or tunes the rules or membership functions of a fuzzy controller with an artificial neural network approach. There are new instantaneous power theories that may address several challenges in power quality. So, this book presents different applications of artificial intelligence techniques in advanced high-tech electronics, such as applications in power electronics, motor drives, renewable energy systems and smart grids.
History of engineering & technology --- droop curve --- frequency regulation --- fuzzy logic --- the rate of change of frequency --- reserve power --- smart grid --- energy Internet --- convolutional neural network --- decision optimization --- deep reinforcement learning --- electric load forecasting --- non-dominated sorting genetic algorithm II --- multi-layer perceptron --- adaptive neuro-fuzzy inference system --- meta-heuristic algorithms --- automatic generation control --- fuzzy neural network control --- thermostatically controlled loads --- back propagation algorithm --- particle swarm optimization --- load disaggregation --- artificial intelligence --- cognitive meters --- machine learning --- state machine --- NILM --- non-technical losses --- semi-supervised learning --- knowledge embed --- deep learning --- distribution network equipment --- condition assessment --- multi information source --- fuzzy iteration --- current balancing algorithm --- level-shifted SPWM --- medium-voltage applications --- multilevel current source inverter --- motor drives --- phase-shifted carrier SPWM --- STATCOM --- electricity forecasting --- CNN–LSTM --- very short-term forecasting (VSTF) --- short-term forecasting (STF) --- medium-term forecasting (MTF) --- long-term forecasting (LTF) --- asynchronous motor --- linear active disturbance rejection control --- error differentiation --- vector control --- renewable energy --- solar power plant --- Data Envelopment Analysis (DEA) --- Fuzzy Analytical Network Process (FANP) --- Fuzzy Theory
Listing 1 - 7 of 7 |
Sort by
|