scholarly journals An Adaptive Random Forest Model for Predicting Demands and Solar Power of a Real Integrated Energy System

2021 ◽  
Author(s):  
Jie Mei ◽  
Christopher Lee ◽  
James L. Kirtley
Keyword(s):  
Random Forest ◽  
Solar Power ◽  
Energy Systems ◽  
Energy System ◽  
Random Forest Model ◽  
Operation Optimization ◽  
Forest Model ◽  
Energy Networks ◽  
Integrated Energy System ◽  
Better Than

In order to address the challenges of improving energy efficiency and integration of renewable energy, multi-energy systems, composed of electric, natural gas, heat and other energy networks, have received more and more attention in recent years and have been rapidly developed. Through integration as a multi-energy system, different energy infrastructures can be scheduled and managed as one unit. One of the main stages in the optimal scheduling of a multi-energy system is the predictions of various demands and sustainable energy in the scheduling horizon. <a>This paper proposes a prediction model based on adaptive random forest for demands and solar power of a real MES, Stone Edge Farm, in California. </a><a>The adaptive random forest model can provide a probability distribution of the prediction results. This allows users to consider a variety of scenarios that may occur in the future for further system operation optimization and help users evaluate the reliability of the results.</a> Besides, an online self-adaptability feature is implemented with the model so it can adapt to the new forecasting environment when new observations are detected. The simulations show that the adaptive random forest model is better than the benchmark models in terms of prediction accuracy.

scholarly journals An Adaptive Random Forest Model for Predicting Demands and Solar Power of a Real Integrated Energy System

2021 ◽  
Author(s):  
Jie Mei ◽  
Christopher Lee ◽  
James L. Kirtley
Keyword(s):  
Random Forest ◽  
Solar Power ◽  
Energy Systems ◽  
Energy System ◽  
Random Forest Model ◽  
Operation Optimization ◽  
Forest Model ◽  
Energy Networks ◽  
Integrated Energy System ◽  
Better Than

In order to address the challenges of improving energy efficiency and integration of renewable energy, multi-energy systems, composed of electric, natural gas, heat and other energy networks, have received more and more attention in recent years and have been rapidly developed. Through integration as a multi-energy system, different energy infrastructures can be scheduled and managed as one unit. One of the main stages in the optimal scheduling of a multi-energy system is the predictions of various demands and sustainable energy in the scheduling horizon. <a>This paper proposes a prediction model based on adaptive random forest for demands and solar power of a real MES, Stone Edge Farm, in California. </a><a>The adaptive random forest model can provide a probability distribution of the prediction results. This allows users to consider a variety of scenarios that may occur in the future for further system operation optimization and help users evaluate the reliability of the results.</a> Besides, an online self-adaptability feature is implemented with the model so it can adapt to the new forecasting environment when new observations are detected. The simulations show that the adaptive random forest model is better than the benchmark models in terms of prediction accuracy.

scholarly journals Urban Population Distribution Mapping with Multisource Geospatial Data Based on Zonal Strategy

2020 ◽  
Vol 9 (11) ◽  
pp. 654
Author(s):  
Guanwei Zhao ◽  
Muzhuang Yang
Keyword(s):  
Random Forest ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Population Distribution ◽  
Random Forest Model ◽  
Geospatial Data ◽  
Forest Model ◽  
Distribution Mapping ◽  
Better Than

Mapping population distribution at fine resolutions with high accuracy is crucial to urban planning and management. This paper takes Guangzhou city as the study area, illustrates the gridded population distribution map by using machine learning methods based on zoning strategy with multisource geospatial data such as night light remote sensing data, point of interest data, land use data, and so on. The street-level accuracy evaluation results show that the proposed approach achieved good overall accuracy, with determinant coefficient (R2) being 0.713 and root mean square error (RMSE) being 5512.9. Meanwhile, the goodness of fit for single linear regression (LR) model and random forest (RF) regression model are 0.0039 and 0.605, respectively. For dense area, the accuracy of the random forest model is better than the linear regression model, while for sparse area, the accuracy of the linear regression model is better than the random forest model. The results indicated that the proposed method has great potential in fine-scale population mapping. Therefore, it is advised that the zonal modeling strategy should be the primary choice for solving regional differences in the population distribution mapping research.

scholarly journals Research on Battery Energy Storage as Backup Power in the Operation Optimization of a Regional Integrated Energy System

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2990 ◽  
Author(s):  
Jianfeng Li ◽  
Dongxiao Niu ◽  
Ming Wu ◽  
Yongli Wang ◽  
Fang Li ◽  
...  
Keyword(s):  
Energy Storage ◽  
Simulation Optimization ◽  
Energy Systems ◽  
Energy System ◽  
Power Source ◽  
Battery Energy Storage ◽  
Operation Optimization ◽  
Integrated Energy Systems ◽  
Integrated Energy System ◽  
Battery Energy

Recently, integrated energy systems have become a new type of energy supply model. It is clear that integrated energy systems can improve energy efficiency and reduce costs. However, the use of a battery energy storage system (BESS) as a backup power source will affect the operating costs of a regional integrated energy system (RIES) in different situations. In this paper, a regional integrated energy system including wind turbines, photovoltaics, gas turbines and battery energy storage was introduced. In order to obtain the minimum operation cost, an operation optimization model was built. The schedule plans of each unit were optimized by a moth flame optimization (MFO) algorithm. Finally, three different scenarios were proposed for the simulation optimization. The simulation optimization results show that when the BESS is used as a backup power source, the operating cost of the system and the resulting pollutant emissions are less than the diesel generator (DG) set. Therefore, it is worthwhile to use BESS instead of DG as the backup power source in RIES.

scholarly journals Operation Optimization of Integrated Energy System under a Renewable Energy Dominated Future Scene Considering Both Independence and Benefit: A Review

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1103
Author(s):  
Jiajia Li ◽  
Jinfu Liu ◽  
Peigang Yan ◽  
Xingshuo Li ◽  
Guowen Zhou ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Renewable Resources ◽  
Energy Systems ◽  
Energy System ◽  
High Ratio ◽  
Grid Security ◽  
Operation Optimization ◽  
Integrated Energy Systems ◽  
Energy Flows ◽  
Integrated Energy System

An integrated energy system interconnects multiple energies and presents a potential for economics improvement and energy sustainability, which has attracted extensive attention. However, due to the obvious volatility of energy demands, most existing integrated energy systems cannot operate in a totally self-sufficient way but interact with the upper grid frequently. With the increasingly urgent demand for energy saving and emissions reduction, renewable resources have occupied a larger and larger proportion in energy system, and at last they may be dominant in the future. Unlike conventional fossil fuel generation, the renewable resources are less controllable and flexible. To ease the pressure and guarantee the upper grid security, a more independent integrated energy system is required. Driven by that, this paper firstly reviews the optimal strategies considering both independence and benefit from perspectives of individual efforts and union efforts. Firstly, the general optimization process is summarized in terms of energy flows modelling and optimization methods to coordinate supply–demand side and realize benefit maximization. Based on that, handling with uncertainty of high-ratio renewable energy is reviewed from uncertainty modeling methods and multi-stage operation strategy perspectives to make the strategy accurate and reduce the adverse effects on the upper grid. Then, the hybrid timescale characteristics of different energy flows are explored to enhance operation flexibility of integrated energy systems. At last, the coordination among different participants is reviewed to reduce the whole adverse effect as a union. Remarks are conducted in the end of each part and further concluded in the final part. Overall, this study summarizes the research directions in operation optimization of integrated energy systems to cater for a renewable energy dominated scene to inspire the latter research.

scholarly journals Clinical trial registries as Scientometric data: A novel solution for linking and deduplicating clinical trials from multiple registries

2021 ◽  
Author(s):  
Christian Thiele ◽  
Gerrit Hirschfeld ◽  
Ruth von Brachel
Keyword(s):  
Clinical Trials ◽  
Random Forest ◽  
Random Forest Model ◽  
Scientometric Analysis ◽  
Data Set ◽  
The Public ◽  
Forest Model ◽  
Clinical Trial Registries ◽  
Multiple Primary ◽  
Clinical Trials Registry

AbstractRegistries of clinical trials are a potential source for scientometric analysis of medical research and serve important functions for the research community and the public at large. Clinical trials that recruit patients in Germany are usually registered in the German Clinical Trials Register (DRKS) or in international registries such as ClinicalTrials.gov. Furthermore, the International Clinical Trials Registry Platform (ICTRP) aggregates trials from multiple primary registries. We queried the DRKS, ClinicalTrials.gov, and the ICTRP for trials with a recruiting location in Germany. Trials that were registered in multiple registries were linked using the primary and secondary identifiers and a Random Forest model based on various similarity metrics. We identified 35,912 trials that were conducted in Germany. The majority of the trials was registered in multiple databases. 32,106 trials were linked using primary IDs, 26 were linked using a Random Forest model, and 10,537 internal duplicates on ICTRP were identified using the Random Forest model after finding pairs with matching primary or secondary IDs. In cross-validation, the Random Forest increased the F1-score from 96.4% to 97.1% compared to a linkage based solely on secondary IDs on a manually labelled data set. 28% of all trials were registered in the German DRKS. 54% of the trials on ClinicalTrials.gov, 43% of the trials on the DRKS and 56% of the trials on the ICTRP were pre-registered. The ratio of pre-registered studies and the ratio of studies that are registered in the DRKS increased over time.

scholarly journals Multi-Scenario Prediction of Intra-Urban Land Use Change Using a Cellular Automata-Random Forest Model

2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Land ◽  
Urban Land Use ◽  
Random Forest Model ◽  
Growth Trend ◽  
Related Factors ◽  
Forest Model

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.

scholarly journals Estimates of daily ground-level NO2 concentrations in China based on Random Forest model integrated K-means

2021 ◽  
pp. 100017
Author(s):  
Xinyu Dou ◽  
Cuijuan Liao ◽  
Hengqi Wang ◽  
Ying Huang ◽  
Ying Tu ◽  
...  
Keyword(s):  
Random Forest ◽  
Ground Level ◽  
Random Forest Model ◽  
Forest Model
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