Multi-Step Ahead Wind Power Generation Prediction Based on Hybrid Machine Learning Techniques
Explore this paper's citation graph
Summary
This study proposed a novel algorithmic solution using various forms of machine learning techniques in a hybrid manner, including phase space reconstruction (PSR), input variable selection (IVS), K-means clustering and adaptive neuro-fuzzy inference system (ANFIS) to improve prediction accuracy compared to benchmark solutions.
- Type
- article
- Published
- 2018-07-30
- Cited by
- 29
- References
- 42
- Access
- Open access
- OpenAlex
- https://openalex.org/W2887747362
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:115874996
Keywords
Adaptive neuro fuzzy inference system, Cluster analysis, Computer science, Particle swarm optimization, Benchmark (surveying)
References
- Electricity price forecasting: A review of the state-of-the-art with a look into the future
- Multistage Wind-Electric Power Forecast by Using a Combination of Advanced Statistical Methods
- Modeling and forecasting of electricity spot-prices: Computational intelligence vs classical econometrics
- Wind speed forecasting for wind farms: A method based on support vector regression
- Applying input variables selection technique on input weighted support vector machine modeling for BOF endpoint prediction
- Feature Selection for Predicting Building Energy Consumption Based on Statistical Learning Method
- Permutation entropy: a natural complexity measure for time series.
- ANFIS: adaptive-network-based fuzzy inference system
- Fine tuning support vector machines for short-term wind speed forecasting
- On comparing three artificial neural networks for wind speed forecasting
- A new wind speed forecasting strategy based on the chaotic time series modelling technique and the Apriori algorithm
- A Comparative Study of Efficient Initialization Methods for the K-Means Clustering Algorithm
- On fuzzy-rough attribute selection: Criteria of Max-Dependency, Max-Relevance, Min-Redundancy, and Max-Significance
- Short-Term Wind Power Ensemble Prediction Based on Gaussian Processes and Neural Networks
- A review on the young history of the wind power short-term prediction
- Standardizing the Performance Evaluation of Short-Term Wind Power Prediction Models
- Comparative Analysis of K-Means and Fuzzy C- Means Algorithms
- A review on time series data mining
- Nonlinear dynamics, delay times, and embedding windows
- Input Feature Selection by Mutual Information Based on Parzen Window
Cited by
- Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p-Power Error Loss
- A Hierarchical Self-Regulation Control for Economic Operation of AC/DC Hybrid Microgrid With Hydrogen Energy Storage System
- Wind power forecasting: A systematic literature review
- Wind Energy Forecasting with Artificial Intelligence Techniques: A Review
- Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods
- Short-Term Direct Probability Prediction Model of Wind Power Based on Improved Natural Gradient Boosting
- Neural-Based Ensembles and Unorganized Machines to Predict Streamflow Series from Hydroelectric Plants
- Adaptive optimal fuzzy logic based energy management in multi-energy microgrid considering operational uncertainties
- Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects
- Short-term Wind Speed Forecasting using Machine Learning Algorithms
- Hour-Ahead Photovoltaic Output Forecasting Using Wavelet-ANFIS
- Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability
- Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives
- Short-Term Wind Power Prediction Based on Data Reconstruction and Improved Extreme Learning Machine
- Hybridization of hybrid structures for time series forecasting: a review
- Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method
- Research on Short-Term Air Conditioning Cooling Load Forecasting Based on Bidirectional LSTM
- Comparison of adaptive neuro-fuzzy inference system (ANFIS) and machine learning algorithms for electricity production forecasting
- Wind power forecasting based on hourly wind speed data in South Korea using machine learning algorithms
- A Selective Review on Recent Advancements in Long, Short and Ultra-Short-Term Wind Power Prediction
Related papers
- Time series prediction based on ensemble ANFIS
- 벨형 퍼지 소속함수를 적용한 ANFIS 기반 퍼지 웨이브렛 신경망 시스템의 연구
- ANFIS and Its Application in Control System
- Improved adaptive neuro-fuzzy inference system with bacterial foraging optimization algorithm for suspended sediment concentration estimation
- Estimation of Long-Term Monthly Temperatures by Three Different Adaptive Neuro-Fuzzy Approaches Using Geographical Inputs
- ANFIS에서 생성된 규칙의 해석용이성 평가
- Study on the adaptive network-based fuzzy inference system and simulation
- A novel method for optimal placing wind turbines in a wind farm using particle swarm optimization (PSO)
- Efficient Feature Selection via Analysis of Relevance and Redundancy
- A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining