Seasonal Analysis and Prediction of Wind Energy Using Random Forests and ARX Model Structures

Abstract

To effectively utilize wind energy, many learningbased autoregressive models have been proposed in the literature. Improving their short-term prediction accuracy, however, is difficult, which mainly result from the stochastic nature of wind. Moreover, the incorporation of seasonal effects to improve their accuracies has not been considered, as most reported studies only relied on relatively short data sets. This brief examines meteorological data that were recorded over a six-year period and contrast various model structures and identification methods proposed in the literature. One focus of this brief is the prediction of wind speed and direction, which has not been extensively studied in the literature but is important for grid management. The reported results highlight that an increase in prediction accuracy can be obtained: 1) by incorporating seasonal effects into the model; 2) by including routinely measured variables, such as radiation and pressure; and 3) by separately predicting wind speed and direction.Index Terms-Autoregressive (AR) data structure, meteorological models, renewable energy, wind direction, wind speed.

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

“…Regarding RF, this is one of the most recent techniques that we have decided to test for this case study, reaching very promising results. Some other interesting examples in the literature use the RF technique to classify data, showing high accuracy compared to other techniques [77]; In other examples [78] RF is used to improve, for instance, prediction of wind energy production in the short term, which is a complicated problem due to the stochastic nature of the wind and using the effects of seasonality. Other interesting examples can be found in [79], where two applications of Decision Trees techniques are presented: the planning of organized energy storage in microgrids and energy control within a PC through the optimal use of local energy resources.…”

Review and Comparison of Intelligent Optimization Modelling Techniques for Energy Forecasting and Condition-Based Maintenance in PV Plants

“…Data mining [13,[21][22][23][24][25] Artificial neural networks [7,[26][27][28][29][30][31][32] Support vector machine [33][34][35][36][37] Decision trees [14,21,[38][39][40][41][42][43] Energies 2019, 12, 4163…”

“…Lin, Y. in [40] uses RF to improve the prediction of wind production in the short term, which is complicated by the stochastic nature of the wind and using the effects of seasonality. RF modelling obtains accurate results in this case.…”

Recent advances in control algorithms for smart structures and machines

“…They conclude that using the road grade preview information as constraint input for the MPC controller improves the fuel efficiency of the vehicle. MPC is also used for distribution of polymers in batch processes (Corbett, Macdonald, & Mhaskar, ), estimation of traffic states in large‐scale urban traffic networks (Hajiahmadi, Haddad, De Schutter, & Geroliminis, ), energy efficiency of office buildings (Sturzenegger, Gyalistras, Morari, & Smith, ), and dampening of vibration and fatigue of wind turbines (Evans, Cannon, & Kouvaritakis, ; Lin, Kruger, Zhang, Wang, Lamont, & Chaar, ).…”