Random forest solar power forecast based on classification optimization

Li, Da; Sun, Kun

doi:10.1016/j.energy.2019.115940

Cited by 124 publications

(27 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, some clustering algorithms based on machine learning models such as the Kmeans method, enable the creation of classes to address environmental problems (Derot et al, 2020;Hartigan and Wong, 1979;Rousseeuw et al, 2014;Solidoro et al, 2007). Furthermore, the predictive performance of an RF model can be improved by utilizing it in conjunction with a K-means model (Kwon and Park, 2016;Liu and Sun, 2019). However, this compels us to use the RF model in the classification mode; however, this is not inconsistent with bio-assessment programs, which are generally used as a system based on the ecological status in the form of categorical data.…”

Section: Introductionmentioning

confidence: 99%

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

2020

View full text Add to dashboard Cite

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

show abstract

Section: Introductionmentioning

confidence: 99%

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

2020

View full text Add to dashboard Cite

show abstract

“…To tackle this problem, a collection of DMTs is utilized to select target values based on average predicted values for all individual trees. Typically, RF follows the bags and boosts strategy in which they integrate different models sharing common information in order to produce several individual trees [ 40 ]. Multiple hyper parameters are required to tune the RF, but the number key parameter is the number of independent trees in the forest.…”

Section: Methodology For Hl and CL Predictionmentioning

confidence: 99%

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Sajjad

Khan

et al. 2020

Sensors

View full text Add to dashboard Cite

In the current technological era, energy-efficient buildings have a significant research body due to increasing concerns about energy consumption and its environmental impact. Designing an appropriate energy-efficient building depends on its layout, such as relative compactness, overall area, height, orientation, and distribution of the glazing area. These factors directly influence the cooling load (CL) and heating load (HL) of residential buildings. An accurate prediction of these load facilitates a better management of energy consumption and enhances the living standards of inhabitants. Most of the traditional machine learning (ML)-based approaches are designed for single-output (SO) prediction, which is a tedious task due to separate training processes for each output with low performance. In addition, these approaches have a high level of nonlinearity between input and output, which need more enhancement in terms of robustness, predictability, and generalization. To tackle these issues, we propose a novel framework based on gated recurrent unit (GRU) that reliably predicts the CL and HL concurrently. To the best of our knowledge, we are the first to propose a multi-output (MO) sequential learning model followed by utility preprocessing under the umbrella of a unified framework. A comprehensive set of ablation studies on ML and deep learning (DL) techniques is done over an energy efficiency dataset, where the proposed model reveals an incredible performance as compared to other existing models.

show abstract

“…Besides, RF illustrates better performance in certain noisy classification or regression problems. Nonetheless, RF creates a lot of trees and combines their outputs, thus requires much more computational power and resources [80], [81].…”

Section: ) Random Forest Regression-based Hybrid Approachmentioning

confidence: 99%

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

et al. 2021

View full text Add to dashboard Cite

Globally, wind energy is growing rapidly and has received huge consideration to fulfill global energy requirements. An accurate wind power forecasting is crucial to achieve a stable and reliable operation of the power grid. However, the unpredictability and stochastic characteristics of wind power affect the grid planning and operation adversely. To address these concerns, a substantial amount of research has been carried out to introduce an efficient wind power forecasting approach. Artificial Intelligence (AI) approaches have demonstrated high precision, better generalization performance and improved learning capability, thus can be ideal to handle unstable, inflexible and intermittent wind power. Recently, AI-based hybrid approaches have become popular due to their high precision, strong adaptability and improved performance. Thus, the goal of this review paper is to present the recent progress of AI-enabled hybrid approaches for wind power forecasting emphasizing classification, structure, strength, weakness and performance analysis. Moreover, this review explores the various influential factors toward the implementations of AI-based hybrid wind power forecasting including data preprocessing, feature selection, hyperparameters adjustment, training algorithm, activation functions and evaluation process. Besides, various key issues, challenges and difficulties are discussed to identify the existing limitations and research gaps. Finally, the review delivers a few selective future proposals that would be valuable to the industrialists and researchers to develop an advanced AI-based hybrid approach for accurate wind power forecasting toward sustainable grid operation.INDEX TERMS Wind power forecasting, artificial intelligence, machine learning, deep learning, optimization, hybrid approaches.

show abstract

Random forest solar power forecast based on classification optimization

Cited by 124 publications

References 42 publications

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

Advances in forecasting harmful algal blooms using machine learning models: A case study with Planktothrix rubescens in Lake Geneva

Towards Efficient Building Designing: Heating and Cooling Load Prediction via Multi-Output Model

Artificial Intelligence Based Hybrid Forecasting Approaches for Wind Power Generation: Progress, Challenges and Prospects

Contact Info

Product

Resources

About