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Adaptive load forecasting of the Hellenic electric grid

Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2008年第12期9卷 1724-1730页

作者： S.Sp.PAPPAS L.EKONOMOU V.C.MOUSSAS P.KARAMPELAS S.K.KATSIKAS Department of Information and Communication Systems Engineering University of the AegeanSamos 83200 Greece Information Technology Faculty Hellenic American UniversityAthens 10680Greece School of Technological Applications Technological Educational Institute of AthensEgaleo 12210Greece Department of Technology Education and Digital Systems University of PiraeusPiraeus 18532Greece

Designers are required to plan for future expansion and also to estimate the grid's future utilization. This means that an effective modeling and forecasting technique, which will use efficiently the information contained in the available data, is required, so that important data properties can be extracted and projected into the future. This study proposes an adaptive method based on the multi-model partitioning algorithm (MMPA), for short-term electricity load forecasting using real data. The grid's utilization is initially modeled using a multiplicative seasonal ARIMA (autoregressive integrated moving average) model. The proposed method uses past data to learn and model the normal periodic behavior of the electric grid. Either ARMA (autoregressive moving average) or state-space models can be used for the load pattern modeling. Load anomalies such as unexpected peaks that may appear during the summer or unexpected faults (blackouts) are also modeled. If the load pattern does not match the normal be-havior of the load, an anomaly is detected and, furthermore, when the pattern matches a known case of anomaly, the type of anomaly is identified. Real data were used and real cases were tested based on the measurement loads of the Hellenic Public Power Cooperation S.A., Athens, Greece. The applied adaptive multi-model filtering algorithm identifies successfully both normal periodic behavior and any unusual activity of the electric grid. The performance of the proposed method is also compared to that produced by the ARIMA model.

关键词： Adaptive multi-model filtering ARIMA Load forecasting Measurements Kalman filter Order selection seasonalvariation Parameter estimation

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Inertial gravity wave parameters for the lower stratosphere from radiosonde data over China

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Science China Earth Sciences 2017年第2期60卷 328-340页

作者： BAI ZhiXuan BIAN JianChun CHEN HongBin CHEN Lei Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth Sciences University of Chinese Academy of SciencesBeijing 100049China Northwest Institute of Nuclear Technology Urumqi 841700China

Characterization of gravity wave （GW） parameters for the stratosphere is critical for global atmospheric circulation models. These parameters are mainly determined from measurements. Here, we investigate variation in inertial GW activity with season and latitude in the lower stratosphere （18-25 km） over China, using radiosonde data with a high vertical resolution over a 2-year period. Eight radiosonde stations were selected across China, with a latitudinal range of 22°-49°N. Analyses show that the GW energy in the lower stratosphere over China has obvious seasonal variation and a meridional distribution, similar to other regions of the globe. The GW energy is highest in winter, and lowest in summer; it decreases with increasing latitude. Velocity perturbations with longitude and latitude are almost the same, indicating that GW energy is horizontally isotropic. Typically, 85% of the vertical wavelength distribution is concentrated between elevations of 1 and 3 km, with a mean value of 2 kin; it is almost constant with latitude. Over 80% of all the horizontal wavelengths occur in the range 100-800 km, with a mean value of 450 km; they show a weak decrease with increasing latitude, yielding a difference of about 40 km over the 22°-49°N range. The ratio of horizontal wavelength over vertical wavelength is about 200：1, which implies that inertial GWs in the lower stratosphere propagate along nearly horizontal planes. Ratios of their intrinsic frequency to the Coriolis parameter decrease with increasing latitude; most values are between 1 and 2, with a mean value of 1.5. Study of the propagation directions of GW energy shows that upward fractions account for over 60% at all stations. In contrast, the horizontal propagation direction is significantly anisotropic, and is mainly along prevailing wind directions; this anisotropy weakens with increasing latitude.

关键词： Inertial gravity wave Lower stratosphere Energy density Intrinsic frequency Propagation direction seasonalvariation Latitudinal variation Velocity perturbations

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Seasonal variation of the global mixed layer depth： comparison between Argo data and FIO-ESM

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Frontiers of Earth Science 2018年第1期12卷 24-36页

作者： Yutong ZHANG Haiming XU Fangli QIAO Changming DONG College of Atmospheric Sciences Nanjing University of Information Science and Technology Nanjing 210044 China College of Marine Sciences Nanjing University of Information Science and Technology-Nanjing 210044 China First Institute of Oceanography State Oceanic Administration Qingdao 266061 China

The present study evaluates a simulation of the global ocean mixed layer depth （MLD） using the First Institute of Oceanography-Earth System Model （FIO- ESM）. The seasonal variation of the global MLD from the FIO-ESM simulation is compared to Argo observational data. The Argo data show that the global ocean MLD has a strong seasonal variation with a deep MLD in winter and a shallow MLD in summer, while the spring and fall seasons act as transitional periods. Overall, the FIO-ESM simula- tion accurately captures the seasonal variation in MLD in most areas. It exhibits a better performance during summer and fall than during winter and spring. The simulated MLD in the Southern Hemisphere is much closer to observations than that in the Northern Hemisphere. In general, the simulated MLD over the South Atlantic Ocean matches the observation best among the six areas. Additionally, the model slightly underestimates the MLD in parts of the North Atlantic Ocean, and slightly overestimates the MLD over the other ocean basins.

关键词： mixed layer depth FIO-ESM model seasonalvariation

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