Multi-time-horizon Solar Forecasting Using Recurrent Neural Network

The non-stationarity characteristic of the solar power renders traditional point forecasting methods to be less useful due to large prediction errors. This results in increased uncertainties in the grid operation, thereby negatively affecting the reliability and increased cost of operation. This research paper proposes a unified architecture for multi-time-horizon predictions for short and long-term solar forecasting using Recurrent Neural Networks (RNN). The paper describes an end-to-end pipeline to implement the architecture along with the methods to test and validate the performance of the prediction model. The results demonstrate that the proposed method based on the unified architecture is effective for multi-horizon solar forecasting and achieves a lower root-mean-squared prediction error compared to the previous best-performing methods which use one model for each time-horizon. The proposed method enables multi-horizon forecasts with real-time inputs, which have a high potential for practical applications in the evolving smart grid.Comment: Accepted at: IEEE Energy Conversion Congress and Exposition (ECCE 2018), 7 pages, 5 figures, code available: sakshi-mishra.github.i

An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.Comment: 19 pages, 12 figures, 3 tables, under review for journal submissio

Solvent Induced Disulfide Bond Formation in 2,5-dimercapto-1,3,4-thiadiazole

Disulfide bond formation is the decisive event in the protein folding to determine the conformation and stability of protein. To achieve this disulfide bond formation in vitro, we took 2,5-dimercapto-1,3,4-thiadiazole (DMcT) as a model compound. We found that disulfide bond formation takes place between two sulfhydryl groups of DMcT molecules in methanol. UV-Vis, FT-IR and mass spectroscopic as well as cyclic voltammetry were used to monitor the course of reaction. We proposed a mechanism for the solvent induced disulfide bond formation on the basis of the results we obtained

Are Recent Segment Disclosures of Indian Firms Useful? An Empirical Investigation

The ultimate objective of the financial statement is the give reliable information, which is to be relevant and therefore useful in economic decisions making. Thus a company which operates in different industrial sectors and geographical areas need to provide information about its various segments and the relative important of each in order to understand the company, the economic environment in which it operates and the development of the situation of the company. Earlier empirical studies carried out in developed countries have documented that disaggregated data published together with the annual report enable analysts, investors and other user groups of company reports to understand better the situation of a firm and to make predictions regarding the companies future profitability with greater accuracy and greater confidence.The results have implications for the investors in Indian stocks, financial analysts and other regulatory bodies such as Institute of Chartered Accountants of India (ICAI), Securities and Exchange Board of India (SEBI), Ministry of Finance (MoF) and Department of Company Affairs (DCA).segment disclosures, Indian financial reporting, segment reporting, AS-17