INTRODUCTION OF BIO- FUNGICIDES FOR CONTROLLING POWDERY MILDEW DISEASE OF RAMBUTAN

Powdery mildew of Rambutan (Nephelium lappaceum) incited by the fungus Oidium nephelii attack young leaves, flowers and immature fruits become dis-colored and dry off causing fruit quality deterioration and heavy fruit losses.  Oidium nephelii is controlled by application of fungicides. An experiment was conducted  to study the efficacy of plant extracts compared with the  fungicides to control powdery mildew disease of Rambutan. The treatments consisted of eight plant extracts (Neem oil, Citronella oil, Cinnamon leaf extract (Cinnamomum zeylanicum), Clove leaf  extract (Syzygium aromaticum),  Naa leaf extract (Mesua nagassarium), Lime leaf extract (Citrus aurantifolia),  Neem seed extract (Azadiracta indica), Vinegar (Cocos nucifera)) and two recommended fungicides Thiovit (wettable  Sulphur) and  Daconil (Chlorothalonil). Severity and spread of powdery mildew basically depends on the environment. Therefore selection of herbal extracts was done during heavy rain and in dry weather conditions. The results obtained from the experiment revealed that under the dry weather condition, the maximum number of healthy fruit set at harvest was given by the Citronella oil and Neem oil. The Clove leaf extract and Cinnamon leaf extract significantly controlled the powdery mildew disease  than that of Department recommended fungicides .Neem seed extract and Vinegar controlled the disease  similar to the Department recommended fungicides. In contrast to the data obtained under dry weather condition, in the heavy rain conditions, only the Citronella oil and Neem oil moderately increased  the healthy fruit set at harvest. The  plant extracts Neem oil, Citronella oil, Clove leaf extract, Cinnamon leaf extract controlled the disease than that of recommended fungicides and they can be used as the bio-fungicides. Bio-fungicides are eco-friendly and are not known to cause pollution to climate or soil

Application of Deep Learning Long Short-Term Memory in Energy Demand Forecasting

The smart metering infrastructure has changed how electricity is measured in both residential and industrial application. The large amount of data collected by smart meter per day provides a huge potential for analytics to support the operation of a smart grid, an example of which is energy demand forecasting. Short term energy forecasting can be used by utilities to assess if any forecasted peak energy demand would have an adverse effect on the power system transmission and distribution infrastructure. It can also help in load scheduling and demand side management. Many techniques have been proposed to forecast time series including Support Vector Machine, Artificial Neural Network and Deep Learning. In this work we use Long Short Term Memory architecture to forecast 3-day ahead energy demand across each month in the year. The results show that 3-day ahead demand can be accurately forecasted with a Mean Absolute Percentage Error of 3.15%. In addition to that, the paper proposes way to quantify the time as a feature to be used in the training phase which is shown to affect the network performance

Optimizing rooftop photovoltaic distributed generation with battery storage for peer-to-peer energy trading

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A multi-agent simulation framework for distributed generation with battery storage

Distributed Generation (DG) is a sustainable alternative energy paradigm that allows flexible customer-participated demand response management, high penetration of renewable sources and reduction of greenhouse gas emission. This paper proposes a multi-agent simulation framework that captures emerging complex responses that originate from individual household behaviors. These behaviors have been unattainable with traditional top-down simulation frameworks. The simulation results demonstrate a suboptimal policy choice may lead to unwanted energy profile responses on the distribution network

Fully densified zircon co-doped with iron and aluminium prepared by sol-gel processing

A sol-gel technique has been used to prepare Fe and Al doped zircon. Structural properties have been studied by X-ray diffraction, nuclear magnetic resonance, scanning electron microscopy and transmission electron microscopy (TEM). Fully densified zircon was produced with high zircon yield and promising microstructures. The presence of Fe promotes zircon formation, while Al improves densification. The zircon phase starts to form at 1215 degrees C, with almost single phase zircon obtained at 1400 degrees C when heated for 1 h. Densification increases very significantly (to 99.7% of theoretical density) when the holding time was increased to 48 h from 1 h. TEM micrographs reveal a crystalline grain boundary phase containing some Fe and Al. (C) 2010 Elsevier Ltd. All rights reserved

Big Energy Data Management for Smart Grids—Issues, Challenges and Recent Developments

Urban areas suffer from tremendous pressure to cope with increasing population in a city. A smart city is a technological solution that integrates engineering and information systems to assist in managing these scarce resources. A smart city comprises several intelligent services such as smart grids, smart education, smart transportation, smart buildings, smart waste management and so on. Among all these, smart grids are the nucleus of all the facilities because these provide sustainable electrical supply for other smart services to operate seamlessly. Smart grids integrate information and communication technologies (ICT) into traditional energy grids, thereby capturing massive amounts of data from several devices like smart meters, sensors, and other electrical infrastructuresSCADA. The data collected in smart grids are heterogeneous and require data analytic techniques to extract meaningful information to make informed decisions. We term this enormous amount of data as big energy data. This book chapter discusses progress in the field of big energy data by enlisting different studies that cover several data management aspects such as data collection, data preprocessing, data integration, data storage, data analytics, data visualisation and decision-making. We also discuss various challenges in data management and report recent progress in this field. Finally, we present open research areas in big data managementBig data management especially in relation to smart grids