Renewable Energy Sources are an effective alternative to the atmosphere-contaminating, rapidly exhausting, and overpriced traditional fuels. However, RESs have many limitations like their intermittent nature and availability at far-off sites from the major load centers. This paper presents the forecasting of wind speed and power using the implementation of the Feedforward and cascaded forward neural networks (FFNNs and CFNNs, respectively). The one and half year’s dataset for Jhimpir, Pakistan, is used to train FFNNs and CFNNs with recently developed novel metaheuristic optimization algorithms, i.e., hybrid particle swarm optimization (PSO) and a Bat algorithm (BA) named HPSOBA, along with a modified hybrid PSO and BA with parameter-inspired acceleration coefficients (MHPSO-BAAC), without and with the constriction factor (MHPSO-BAAC-χ). The forecasting results are made for June–October 2019. The accuracy of the forecasted values is tested through the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The graphical and numerical comparative analysis was performed for both feedforward and cascaded forward neural networks that are tuned using the mentioned optimization techniques. The feedforward neural network was achieved through the implementation of HPSOBA with a mean absolute error, mean absolute percentage error, and root mean square error of 0.0673, 6.73%, and 0.0378, respectively. Whereas for the case of forecasting through a cascaded forward neural network, the best performance was attained by the implementation of MHPSO-BAAC with a MAE, MAPE and RMSE of 0.0112, 1.12%, and 0.0577, respectively. Thus, the mentioned neural networks provide a more accurate prediction when trained and tuned through the given optimization algorithms, which is evident from the presented results.
Summary
Several techniques for a state‐of‐the‐art advanced metering infrastructure (AMI) within the smart grid are being extensively studied and evaluated around the world. Advanced metering infrastructure provides an enhanced and efficient energy management infrastructure that can facilitate the latest demands of electric utilities, consumers, and smart grid. In the past few years, standardization process for the implementation of AMI using narrowband power line communications (NB‐PLC) transceivers has been completed by ITU‐T and IEEE. This paper is focused on the utilization of CENELEC band of NB‐PLC for deployment aspects. The power transformer model of 200 and 75 kVA transformers for 9 to 90 kHz frequency range that lies in CENELEC‐A band of NB‐PLC is also proposed. The proposed model is more simple than previously presented models in the literature and has the capability to evaluate the transformers working not only under normal condition but also under resonance condition for frequency of interest. Moreover, the efficient procedure for field measurements that provides a generalized NB‐PLC channel model for low voltage access network to deploy AMI within Lahore, Pakistan, is presented. A Simulink model is developed to validate the results. The analysis of measured and simulated results illustrates a close agreement of channel transfer functions. This research work proposes an improved and more accurate NB‐PLC channel model especially for South Asian countries.
For the enzymatic saccharification of canola meal by enzyme preparations from Trichoderma reesei as well as by commercially available hemicellulase and multienzyme preparations, a pretreatment consisting of autoclaving is necessary. These enzyme preparations hydrolysed over 20% (w/w) of pretreated canola meal, which constitutes over 70% saccharification of the total polysaccharides present in canola meal. The results show that saccharification of canola meal is mainly brought about by hemicellulases capable of degrading arabinogalactan, arabinoglucan, galactan and galactomannan, while cellulases and xylanases play a minor role. These hemicellulases were found to be more stable at 50°C than cellulases or xylanases. This pretreatment also released water‐soluble polysaccharides consisting mainly of arabinose and glucose. Trichoderma reesei was unable to produce enzymes capable of hydrolysing this polysaccharide when cultivated on canola meal as substrate.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.