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

Hysteroscopic evaluation of uterine cavity in cases of infertility and its correlation with transvaginal ultrasound and hysterosalpingography

Background: Infertility is defined as one year of unprotected intercourse without pregnancy. This study was taken up to evaluate the diagnostic accuracy of hysteroscopy in comparison with hysterosalpingography and vaginal ultrasound in the evaluation of the uterine cavity as first line study in the infertile patient.Methods: A Prospective and comparative study was carried out in the department of Obstetrics and Gynaecology, S.S. Medical College and associated Gandhi Memorial Hospital, Rewa, MP, India in a period of 13 months from August 2014 to September 2015 conducted on 60 subjects.Results: 60 patients were evaluated with diagnosis of primary and secondary infertility. Hysteroscopy showed alterations in 65%, predominantly uterine synechiae, chronic endometritis and endometrial polyp. Hysterosalpingography reported a sensitivity of 90% and a specificity of 100%, with a positive predictive value of 100% and a negative predictive value of 66.6%. The agreement between the two methods was moderate. The transvaginal ultrasound reported a sensitivity of 51.21% and a specificity of 100%, the agreement between these two procedures was moderate. There were no complications during hysteroscopy.Conclusions: We believe that transvaginal ultrasound, hysterosalpingography and hysteroscopy are complementary in the evaluation of the infertile patient but Hysteroscopy can diagnose small intrauterine lesions much more precisely, (compared with HSG and even TVS) and treat them simultaneously. Thus we consider routine hysteroscopy should be included in the evaluation of the infertile couple.

Multi-operator Differential Evolution with MOEA/D for Solving Multi-objective Optimization Problems, Journal of Telecommunications and Information Technology, 2022, nr3

In this paper, we propose a multi-operator differentia evolution variant that incorporates three diverse mutation strategies in MOEA/D. Instead of exploiting the local region, the proposed approach continues to search for optimal solutions in the entire objective space. It explicitly maintains diversity of the population by relying on the benefit of clustering. To promowe convergence, the solutions close to the ideal position, in the objective space are given preference in the evolutionary process. The core idea is to ensure diversity of the population by applying multiple mutation schemes and a faster convergence rate, giving preference to solutions based on their proximity to the ideal position in the MOEA/D paradigm. The performance of the proposed algorithm is evaluated by two popular test suites. The experimental results demonstrate that the proposed approach outperforms other MOEA/D algorithms

An IP Core of AMBA Bus Interface in HDL

The AMBA on-chip bus architecture is a well-known open specification that explains how to connect and manage the functional units that make up a System-On-Chip (SoC). The design and implementation of an AHB Master, RAM, ROM, FIFO and Memory Controller implementation is proposed in this paper. It is primarily divided into two categories: operation initiator (AHB MASTER) and AHB SLAVE. Furthermore, AHB master generate the operation in burst mode, single transfer according to interface requirement and Address generator, generates the address in increment or wrap mode, as well as completing data transfers with an asymmetric asynchronous FIFO with variable data widths for read and write. A bridge between an AHB Master and an AHB slave will be demonstrated using a memory controller, and their outcome in terms of area and speed will be address ed. A finite state machine will be used to design the control framework. Xilinx Virtex 2 XC2VP40 will be used to implement the AHB Master and Slave IP