Short-term wind speed forecasting based on random forest model combining ensemble empirical mode decomposition and improved harmony search algorithm

ABSTRACT

Wind speed forecasting plays an important role in power grid dispatching management. This article proposes a short-term wind speed forecasting method based on random forest model combining ensemble empirical modal decomposition and improved harmony search algorithm. First, the initial wind speed data set is decomposed into several ensemble empirical mode functions by EEMD, then feature extraction of each sub-modal IMF is performed using fast Fourier transform to solve the cycle of each sub-modal IMF. Next, combining the high-performance parameter optimization ability of the improved harmony search algorithm, two optimal parameters of random forest model, number of decision trees, and number of split features are determined. Finally, the random forest model is used to forecast the processing results of each submodal IMF. The proposed model is applied to the simulation analysis of historical wind data of Chaoyang District, Liaoning Province from April 27, 2015 to May 22, 2015. To illustrate the suitability and superiority of the EEMD-RF-IHS model, three types of models are used for comparison: single models including ANN, SVM, RF; EMD combination models including EMD-ANN, EMD-SVM, EMD-RF; EEMD combination models including EEMD-ANN, EEMD-SVM, EEMD-RF. The analysis results of evaluation indicators show that the proposed model can effectively forecast short-term wind data with high stability and precision, providing a reference for forecasting application in other industry fields.     

Multi-objective-based optimal transmission switching and demand response for managing congestion in hybrid power systems

ABSTRACT

This paper proposes a novel congestion management (CM) approach by using the optimal transmission switching (OTS) and demand response (DR) for a system with conventional thermal generators and renewable energy sources (RESs). In this paper, wind and solar PV units are considered as the RESs. The stochastic behavior of wind and solar PV powers are modeled by using the appropriate probability density functions (PDFs). The proposed CM methodology simultaneously optimizes the generation dispatch, demand response, and also the network topology of the power system. The OTS identifies the branches that should be taken out of service by significantly reducing the operating cost of the system while respecting the system security. Here, the total operating cost minimization/social welfare maximization and system losses minimization are considered as the objectives to be optimized. The proposed CM problem is solved using the multi-objective Jaya algorithm and it is used to determine a set of Pareto-optimal solutions. The Jaya algorithm is simple and it does not have any algorithmic-specific parameters to be tuned. This aspect reduces the designer’s effort in tuning the parameters to arrive at the optimum objective function value. A fuzzy logic-based approach is used to identify the best compromise solution. The effectiveness of the proposed CM approach is examined on modified IEEE 30 and practical Indian 75 bus test systems. The obtained simulation results are analyzed and they show the effectiveness of the proposed approach.     

Investigation of sustainable energy alternatives for powering remote communities in northern Ontario

ABSTRACT

Remote communities in the North of Ontario survive in isolation as their proximity to the southern industrial sector of the province limits their accessibility to the major grid. The lack of grid connection has led to antiquated methods of power generation which pollute the environment and deplete the planet of its natural resources. Aside from the primary means of electricity generation being by diesel generators, generation infrastructure is deteriorating due to age and the stagnation of the power supply has led to communities facing load restrictions. These challenges may be resolved by introducing clean energy alternatives and providing a fuel blend option. The primary energy sources investigated in this research are solar, wind, and hydrogen. To assess the viability of these energy production methods in Northern communities, an exergy analysis is employed as it utilizes both the first and second law of thermodynamics to determine systems’ efficiency and performance in the surroundings. Local weather patterns were used to determine the viability of using wind turbines, solar panels and/or hydrogen fuel cells in a remote community. Through analysis of the resources available at the community, it was determined that the hydrogen fuel cell was best suited to provide clean energy to the community. Wind resulted in low efficiency in the range of 2–3% while solar efficiencies resulted in ranges of 18 – 19%, as the seasonal variations between the three years is not very great. Due to the higher operating efficiencies observed of the PV panels it would also be an attractive alternative to diesel generators however, the lack of consistent operation above 30% efficiency throughout the year, resulted in hydrogen fuel cells being a better alternative.     

The hybrid model of empirical wavelet transform and relevance vector regression for monthly wind speed prediction

ABSTRACT

In order to improve the prediction ability for the monthly wind speed of RVR, the hybrid model of empirical wavelet transform and relevance vector regression (EWT-RVR) is proposed for monthly wind speed prediction in this study. Compared with empirical mode decomposition (EMD), empirical wavelet transform (EWT) can obtain a more consistent decomposition and have a mathematical theory. In order to testify the superiority of EWT-RVR, several traditional RVR models are used to compare with the proposed EWT-RVR method under the situation of the same embedding dimensions. The experimental results show that the proposed EWT-RVR method has a better prediction ability for monthly wind speed than RVR. It can be concluded that the proposed EWT-RVR method for monthly wind speed is effective.     

中国热带近百年气候波动与自然灾害

20世纪，中国热带气温呈波动上升趋势。在气温波动过程中有2个冷期和2个暖期。最暖的时期为80年代后，这与全球变化相一致，但是昆明最暖的时期为40年代。气候型大多为暖湿同期，这与全国其它区域的状况不同。自然灾害有加剧的趋势：台风次数增多，多台风年洪水多发，旱灾和冷害次数增多，广州曾降微雪，海口和崖县曾见霜，昆明曾有4年强降雪。     

公路隧道火灾事故调研与对策分析

通过调研国内外近60年来,有代表性的33起公路隧道火灾事故,对公路隧道火灾事故起因、特点及危害进行了详细分析,结合国外公路隧道火灾安全措施以及防火安全评估经验,提出几点降低公路隧道火灾事故风险,减少事故危害的对策与建议。以期为公路隧道规划、设计以及建设运营等工作提供有益的参考。     

隧道火灾拱顶附近烟气最高温度的研究

为了研究顶部开口的城市隧道在采用自然通风模式下的火灾特性,在已建成的隧道中设计并实施了全尺寸火灾试验,得到了隧道火灾自然通风模式下的拱顶附近烟气最高温度纵向变化数据。通过实验数据与理论预测结果对比的方法验证了H.Kurioka等人建立的隧道火羽流模型及其计算公式的可靠性,为市政公路隧道建设提供了科学依据,为隧道火灾的研究及其消防工作提供理论指导和有益借鉴。     

A TREE-RING RECONSTRUCTION OF DROUGHT IN SOUTHERN CALIFORNIA1

ABSTRACT: Indices of annual diameter growth of trees were used to reconstruct drought in southern California back to A.D. 1700. A regional Palmer Drought Index served as predictand and tree-ring indices from eight sites as predictors in multiple linear regression analyses that yielded the prediction (reconstruction) equations. The regression explained 69 percent of the variance in Palmer Index in the period of calibration. The long-term reconstruction indicated that drought was rare in the first half of the current century relative to other discrete 50-year periods, and that based on evidence to date the last half of the 20th century may well turn out to be the most drought prone since A.D. 1700 in southern California.     

隧道排污量估算与治污效果预测

通过隧道排污量和排污效果的简单分析，指出了其排污治理过程中应注意的有关事项，如进 行人口控制，改善居民的生活方式，提高环境保护意识和治理技术等，所述分析方法可供其它类似 工程参考。     

苏南地区CO2本底浓度及源汇特征

基于2015~2018年苏州张家港站CO₂在线观测数据，采用时序检查、选取稳定性数据、异常值剔除等质量控制方法获得可靠数据，并通过平均移动过滤（MAF）本底筛分法获得本底数据，讨论苏南地区CO₂变化特征.结果发现：CO₂本底浓度日变化为单峰结构，谷值和峰值分别出现在下午15：00和凌晨5：00前后；季节变化为双峰结构，峰值分别出现在12月和4月；日、季节变化的分布特征均与陆地生态系统、气象条件和人类活动有关.此外，2015~2018年CO₂浓度呈逐年上升趋势，抬升浓度占比逐年增加，吸收浓度占比波动较小，表明人类活动对CO₂浓度的影响正在逐年增加；而陆地生态系统对CO₂吸收汇的作用则相对稳定.源汇分析显示，CO₂抬升浓度随季节小幅波动；吸收浓度则夏半年较低，冬半年较高；抬升浓度日变化为单峰结构，谷值和峰值分别出现在15：00和8：00前后，早晨正值上班高峰，机动车排放可能为早晨峰值的主要因素；吸收浓度日间低、夜间高，这主要与植物光合作用及对流输送有关.分析CO₂浓度与风的关系发现，所有季节静风情况下，CO₂浓度偏高均最为明显，大部分方向CO₂浓度高低与风速大小有明显的负相关，其中S~WNW方向偏高最为明显，这可能是因为SW~NW方向主要为内陆城市群，且测站周边建筑区主要位于W~N方向，弱风有利于本地排放累积的结果.此外，WNW方向风速较大时浓度仍偏高明显，可能与测站W~N方向为建筑区及内陆城市群有关；而测站偏东方向主要为农田和林区，受人类活动影响较小，且海上气流较为洁净，故偏东风较弱时浓度也不高；说明了CO₂浓度除了与风速大小有关外，与周边下垫面类型及较远距离环境特征（城市群或海洋）也有一定的关系.