Multi-objective particle swarm optimization algorithm for multi-step electric load forecasting

As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability

Do Connections with Buy-Side Analysts Inform Sell-Side Analyst Research?

Prior research suggests that private information developed by an institutional investor’s buy-side analysts becomes public only through observation of fund manager trading decisions. We identify another pathway. Specifically, we hypothesize that buy-side analyst connections with sell-side analysts offer the sell-side a view of the buy-side’s private information, thus enhancing the quality of sell-side research output. We proxy for these connections with the weighted number of stocks at the intersection of stocks held in the portfolios of institutional investors and followed by the sell-side analyst. The larger this intersection, the more opportunities the sell-side analyst has to interact with institutional investors. We proxy for the research quality of the sell-side analyst with her earnings forecast accuracy. We find that such connections enhance the accuracy of earnings forecasts, but up to a point of diminishing returns. Additional tests rule out reverse causality and omitted variables as explanations for the association and strengthen the inference that connections between sell- and buy-side analysts increase the flow of information and improve the quality of sell-side research output

Numerical simulation on the damage of buried thermal-pipeline under seismic loading based on thermal-mechanical coupling

AbstractThe thermal-mechanical coupling effect is one of important factors in construction engineering, which will cause the buried thermal-pipeline to damage. So a three-dimensional finite element model is established based on ADINA-TMC, which considers thermal-mechanical coupling and seismic loading simultaneously. In this model, seismic loads and faults movement are defined. According to the numerical results, stresses and strains under gravity, seismic loading, and temperature load are compared, which provides theoretical method for failure analysis of buried thermal-pipeline

A nonlocal curve flow in centro-affine geometry

In this paper, the isoperimetric inequality in centro-affine plane geometry is obtained. We also investigate the long-term behavior of an invariant plane curve flow, whose evolution process can be expressed as a second-order nonlinear parabolic equation with respect to centro-affine curvature. The forward and backward limits in time are discussed, which shows that a closed convex embedded curve may converge to an ellipse when evolving according to this flow

Intelligent Optimized Combined Model Based on GARCH and SVM for Forecasting Electricity Price of New South Wales, Australia

Daily electricity price forecasting plays an essential role in electrical power system operation and planning. The accuracy of forecasting electricity price can ensure that consumers minimize their electricity costs and make producers maximize their profits and avoid volatility. However, the fluctuation of electricity price depends on other commodities and there is a very complicated randomization in its evolution process. Therefore, in recent years, although large number of forecasting methods have been proposed and researched in this domain, it is very difficult to forecast electricity price with only one traditional model for different behaviors of electricity price. In this paper, we propose an optimized combined forecasting model by ant colony optimization algorithm (ACO) based on the generalized autoregressive conditional heteroskedasticity (GARCH) model and support vector machine (SVM) to improve the forecasting accuracy. First, both GARCH model and SVM are developed to forecast short-term electricity price of New South Wales in Australia. Then, ACO algorithm is applied to determine the weight coefficients. Finally, the forecasting errors by three models are analyzed and compared. The experiment results demonstrate that the combined model makes accuracy higher than the single models

Study on Thermal Comfort for University Classrooms in Pre- Heating Season in Xi\u27an

Thermal comfort of students in university classrooms during transition season in Xi\u27an, before heating, is studied. Indoor thermal environment parameters and outdoor weather parameters of seven typical classrooms in a university campus in Xi\u27an were measured. At the same time, the subjective questionnaires were used to know students\u27 satisfaction and expectation with various environmental factors. 992 valid questionnaires were received. Based on the data collected, the thermal comfort of occupants in classroom was discussed and a thermal comfort adaptive model was established. The results show that the range of thermal comfort acceptable to students is broader than that defined in the ASHARE standard, indicating that students have some adaptability to indoor air environment. The measured indoor thermal neutral temperature is lower than the theoretical one. There is difference between the thermal sensation vote (TSV) and the predicted mean vote (PMV). The slope of TSV cure vs. operative temperature is greater than that of PMV, indicating that under actual condition, students are more sensitive to air changes. The proposed adaptive model provided a reference for understanding the thermal comfort of university buildings under natural ventilation environment in Xi’an, helpful to improve the thermal comfort and save energy for university buildings in Xi’an