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

Solar-Powered Plant Protection Equipment: Perspective and Prospects

The major challenges in sustainable and profitable agriculture are developing high-yielding crop varieties and reducing crop losses. Presently, there are significant crop losses due to weed/bird/insect/animal attacks. Among the various renewable energy sources, solar energy is utilized for different agricultural operations, especially in plant protection applications. Solar photovoltaic (PV) devices present a positive approach to sustainable crop production by reducing crop loss in various ways. This might result in the extensive use of PV devices in the near future. PV-based plant protection equipment/devices are primarily utilized in protecting crops from birds, weeds, or insects. Solar-powered plant protection equipment such as light traps, bird scarers, sprayers, weeders, and fencing are gaining interest due to their lower operational costs, simple design, no fuel requirements, and zero carbon emissions. Most of these PV devices require 12 V rechargeable batteries with different currents to meet the load, which varies from 2 to 1500 W. This paper briefly discusses the applications of solar-powered plant protection devices in sustainable agriculture and their future prospects

Identification of a C2-symmetric diol based human immunodeficiency virus protease inhibitor targeting Zika virus NS2B-NS3 protease

Zika virus (ZIKV) is an emerging mosquito-borne flavivirus and infection by ZIKV Asian lineage is known to cause fetal brain anomalies and Guillain-Barrés syndrome. The WHO declared ZIKV a global public health emergency in 2016. However, currently neither vaccines nor antiviral prophylaxis/treatments are available. In this study, we report the identification of a C2-symmetric diol-based Human immunodeficiency virus type-1 (HIV) protease inhibitor active against ZIKV NS2B-NS3 protease. The compound, referred to as 9b, was identified by in silico screening of a library of 6265 protease inhibitors. Molecular dynamics (MD) simulation studies revealed that compound 9b formed a stable complex with ZIKV protease. Interaction analysis of compound 9b's binding pose from the cluster analysis of MD simulations trajectories predicted that 9b mostly interacted with ZIKV NS3. Although designed as an aspartyl protease inhibitor, compound 9b was found to inhibit ZIKV serine protease in vitro with IC50 = 143.25 ± 5.45 µM, in line with the in silico results. Additionally, linear interaction energy method (LIE) was used to estimate binding affinities of compounds 9b and 86 (a known panflavivirus peptide hybrid with IC50 = 1.64 ± 0.015 µM against ZIKV protease). The LIE method correctly predicted the binding affinity of compound 86 to be lower than that of 9b, proving to be superior to the molecular docking methods in scoring and ranking compounds. Since most of the reported ZIKV protease inhibitors are positively charged peptide-hybrids, with our without electrophilic warheads, compound 9b represents a less polar and more drug-like non-peptide hit compound useful for further optimization.Communicated by Ramaswamy Sarma

Solar-Powered Plant Protection Equipment: Perspective and Prospects

The major challenges in sustainable and profitable agriculture are developing high-yielding crop varieties and reducing crop losses. Presently, there are significant crop losses due to weed/bird/insect/animal attacks. Among the various renewable energy sources, solar energy is utilized for different agricultural operations, especially in plant protection applications. Solar photovoltaic (PV) devices present a positive approach to sustainable crop production by reducing crop loss in various ways. This might result in the extensive use of PV devices in the near future. PV-based plant protection equipment/devices are primarily utilized in protecting crops from birds, weeds, or insects. Solar-powered plant protection equipment such as light traps, bird scarers, sprayers, weeders, and fencing are gaining interest due to their lower operational costs, simple design, no fuel requirements, and zero carbon emissions. Most of these PV devices require 12 V rechargeable batteries with different currents to meet the load, which varies from 2 to 1500 W. This paper briefly discusses the applications of solar-powered plant protection devices in sustainable agriculture and their future prospects