Spatio-temporal Markov chain model for very-short-term wind power forecasting

Wind power forecasting (WPF) is crucial in helping schedule and trade wind power generation at various spatial and temporal scales. With increasing number of wind farms over a region, research focus of WPF methods has been recently moved onto exploring spatial correlation among wind farms to benefit forecasting. In this study, a spatio-temporal Markov chain model is proposed for very-short-term WPF by extending the traditional discrete-time Markov chain and incorporating off-site reference information to improve forecasting accuracy of regional wind farms. Not only are the transitions between the power output states of the target wind farm itself considered in the forecasting model, but also the transitions from the output states of reference wind farms to that of the target wind farm are introduced. The forecasting results derived from multiple spatio-temporal Markov chains regarding different reference wind farms over the same region are optimally weighted using sparse optimisation to generate forecasts of the target wind farm. The proposed method is validated by comparing with both local and spatio-temporal WPF methods, using a real-world dataset

Baseline differences in metabolic profiles of patients with lung squamous cell carcinoma responding or not responding to treatment with nanoparticle albumin-bound paclitaxel (nab-paclitaxel)

Background: Nanoparticle albumin-bound paclitaxel (nab-paclitaxel) is a preparation widely used in chemotherapy for cancers. However, only some patients benefit from this treatment. Therefore, identifying which patients will respond to nab-paclitaxel therapy is crucial. Methods: A cohort of 32 patients with lung squamous cell carcinoma (LUSC) treated with nab-paclitaxel were enrolled in this study. Plasma samples were collected before chemotherapy and used to perform metabolomic and lipidomic analyses. Tumor response to two cycles of chemotherapy was evaluated. Metabolites differentially present among populations were screened and analyzed. Results: According to the RECIST criteria, one-third of patients had a significant response to nab-paclitaxel, whereas one-fifth showed no discernible benefit. According to the criteria of variable importance in projection >1 and fold change >2, we identified 61, 81 and 54 differential metabolites between the progressive disease (PD) vs partial response (PR), PD vs stable disease (SD), and SD vs PR groups, respectively. Moreover, we used three variation in logistic regression models and ROC diagnostic curves to identify optimal metabolites for stratifying patients with differing chemotherapeutic responses. The PD vs SD, SD vs PR, and PD vs PR groups were well separated on the basis of cis-9,10-epoxystearic acid/octapentaenoic acid (AUC 0.9330), salicyluric acid/DG (18:1/20:5/0:0) (AUC 1.0000) and D-glyceric acid/9,12-octadecadienoic acid (AUC 1.0000), respectively. Conclusion: The baseline metabolic profiles significantly differed between responder and non-responder patients with LUSC treated with nab-paclitaxel. These differential metabolites have the potential to predict the outcomes of patients with LUSC before chemotherapy

Association of Antioxidative Enzymes Polymorphisms with Efficacy of Platin and Fluorouracil-Based Adjuvant Therapy in Gastric Cancer

Background/Aims: Imbalance of oxidative/antioxidative enzymes in cells is associated with carcinogenesis and cancer cell chemoresistance. The aim of this study was to examine the clinical significance of potentially functional single nucleotides polymorphisms (SNPs) in antioxidative enzymes, GPxs and CAT, in stages II and III gastric cancer patients. Methods: A total of 591 gastric cancer patients who had radical gastrectomy were recruited. 207 patients received platinum and fluorouracil-based (PF-based) adjuvant chemotherapy and 384 patients were untreated. GPx1 rs1050450, GPx2 rs4902346, GPx3 rs736775, rs3828599 and CAT rs769218 were genotyped in the DNA samples extracted from paraffin-embedded tumor tissue. Results: CAT rs769218 was significantly correlated with the overall survival (OS) in the dominant model (P = 0.014). Multivariate analysis revealed that CAT rs769218 GA/AA (HR, 0.715; 95%CI, 0.562-0.910, P = 0.006) was an independent prognostic marker indicating improved survival. After adjustments, GPx3 rs736775 TC/CC was significantly associated with improved OS (HR, 0.621; 95%CI, 0.399-0.965; P=0.034) in patients treated with PF-based adjuvant chemotherapy, and CAT rs769218 GA/AA was significantly associated with improved OS (HR, 0.646; 95% CI, 0.482-0.864; P = 0.003) in the untreated patients. PF-based chemotherapy significantly decreased risk of death for patients carrying GPx3 rs736775 TC/CC and age ≤ 60 years or with diffused type adenocarcinoma compared to surgery alone. Conclusion: our findings suggested CAT rs769218 and GPx3 rs736775 may be considered as prognostic markers in gastric cancer. Patient stratification by GPx3 rs736775 and conventional pathological parameters may provide additional predictive information in treatment decision-making

A novel spatio-temporal wind power forecasting framework based on multi-output support vector machine and optimization strategy

The integration of a large number of wind farms poses big challenges to the secure and economical operation of power systems, and ultra-short-term wind power forecasting is an effective solution. However, traditional approaches can only predict an individual wind farm power at a time and ignore the spatio-temporal correlation of wind farms. In this paper, a novel ultra-short-term forecasting framework based on spatio-temporal (ST) analysis, multi-output support vector machine (MSVM) and grey wolf optimizer (GWO) which defined ST-GWO-MSVM model is proposed to predict the output wind power from multiple wind farms; the ST-GWO-MSVM model includes data analysis stage, parameters optimization stage, and modeling stage. In the data analysis stage, the person correlation coefficient and partial autocorrelation function are used to analyze the spatio-temporal correlation of wind power. In the parameters optimization stage, to avoid obtaining the unreliable forecasting results due to the parameters are chosen empirically, the GWO algorithm is used to optimize the kernel function parameters of the MSVM model. In the modeling stage, an innovative forecasting model with optimal parameter of MSVM is proposed to predict the output wind power of 15 wind farms. Results show that the performance of ST-GWO-MSVM is better than other benchmark models in terms of multiple-error metrics including fractional bias, direction accuracy, and improvement percentages

Hierarchical model predictive control strategy based on dynamic active power dispatch for wind power cluster integration

Large-scale wind power cluster with distributed wind farms has generated the active power dispatch and control problems in the power system. In this paper, a novel hierarchical model predictive control (HMPC) strategy based on dynamic active power dispatch is proposed to improve wind power schedule and increase wind power accommodation. The strategy consists of four layers with refined time scales, including intra-day dispatch, real-time dispatch, cluster optimization and wind farm modulation layer. A dynamic grouping strategy is specifically developed to allocate the schedule for wind farms in cluster optimization layer. In order to maximize wind power output, downward spinning reserve and transmission pathway utilization are developed in wind farm modulation layer. Meanwhile, a stratification analysis approach for ultra-short-term wind power forecasting error is presented as feedback correction to increase forecasting accuracy. The proposed strategy is evaluated by a case study in the IEEE network with wind power cluster integration. Results show that wind power accommodation has been enhanced by use of the proposed HMPC strategy, compared with the conventional dispatch and allocation methods