Long-Term Electricity Load Forecasting Based On Cascade Forward Backpropagation Neural Network

Nowadays, the Electrical System has an important role in all sectors of life. Electricity has a strategic role. Accuracy and reliability in electricity load forecasting is a great key that can help electricity companies in supplying electricity efficiency, hence, reducing wasted energy. In addition, electricity load forecasting can also help electricity companies to determine the purchase price and power generation. Long-term forecasting is a method of forecasting with a span of more than one year. The historical data will be a reference in solving the problems. This research propose the concept of cascade forward backpropagation for long-term load forecasting. The advantage of this concept is that it can accommodate non-linear conditions without ignoring the linear conditions. This study compared the results of the original data, Feed Forward Backpropagation Neural Network (FFBNN) and Cascade Forward Backpropagation Neural Network (CFBNN). The results were measured by comparing Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE)

Grey-Fuzzy Hybrid Optimization and Cascade Neural Network Modelling in Hard Turning of AISI D2 Steel

Nowadays hard turning is noticed to be the most dominating machining activity especially for difficult to cut metallic alloys. Attributes of dry hard turning are highly influenced by the amount of heat generation during cutting. Some major challenges are rapid tool wear, lower tool-life span, and poor surface finish but simultaneously generated heat is enough to provide thermal softening of hard work material and facilitates easier shear deformation thus easy cutting. Also, plenty of works reported the utilization of various cooling methods as well as coolants which successfully retard the intensity of cutting heat but this leads to additional cost as well as environmental and health issues. However, still, there is scope to select proper cutting tool materials, its geometry, and appropriate values of cutting parameters to get favorable machining outcomes under dry hard turning and avoid the cooling cost, environmental and health issue. Considering these challenges, current work utilizes PVD-coated (TiAlN) carbide insert in dry hard turning of AISI D2 steel. The multi-responses like tool-flank wear, chip morphology and chip reduction coefficient are considered. Further, to get the best combination of input cutting terms, grey-fuzzy hybrid optimization (Type I and Type II) is utilized considering the Gaussian membership function. Type II grey-fuzzy system attributed to 15 % less error (between GRG and GFG) compared to Type I. Hence, Type II grey-fuzzy system is utilized to get the optimal set of input terms. The optimal combination of input terms is found as t-1 (0.15 mm), s-4 (0.25 mm/rev) and is Vc-2 (100 m/min) which is comparable to the results obtained under spray impingement cooling using CVD tool in the literature. However, hard turning can be assessed under the dry condition with a PVD tool at the obtained optimal input condition for industrial uses. Further, six different types of cascade-forward-back propagation neural network modelling are accomplished. Among all models, CFBNN-4 model exhibited the best prediction results with a mean absolute error of 2.278% for flank wear (VBc) and 0.112% for the chip reduction coefficient (CRC). However, this model can be recommended for other engineering modelling problems

Forecasting Performance Of Cascade Forward Back Propagation Neural Network For Data With Outliers

Dalam kajian ini, satu rangkaian neural berasaskan pengelompokan telah dibangunkan untuk menyiasat dan membandingkan prestasinya dengan teknik-teknik pe- modelan lain bagi kes penyimpangan andaian berkaitan hubungan homoskedastik dalam set data In this research, a clustering based neural network was developed with the aim of investigating and comparing its performance with the performance of other model techniques in the case of deviation from the assumption of homoscedastic relation- ship in datase

PERAMALAN BEBAN LISTRIK HARIAN MENGGUNAKAN ARTIFICIAL NEURAL NETWORK

PERAMALAN BEBAN LISTRIK HARIAN MENGGUNAKAN ARTIFICIAL NEURAL NETWOR

Der Begriff Gerechtigkeit im Alten Testament, besonders in den Psalmen

Der Begriff Gerechtigkeit im Alten Testament, besonders in den Psalmen (The term righteousness in the Old Testament, especially in the Psalms

Approximate distribution of the likelihood ratio test for testing the equality of two means with equal coefficients of variation

Simulation and tables -- Smoothed approximate critical values for the test statistic -- Fonction de répartition de Lamboa simulée

Application of field theoretical methods to problems in mesoscopic physics

The subject of this thesis are three different topics related to quantum interference and dephasing in weakly disordered mesoscopic systems. The first topic covers the so-called ``current echo", which is a quantum-interference phenomenon predicted about a decade ago by Thomas et al. on the basis of numerical calculations. Our motivation to study the current echo originates from the fact that a simple physical picture explaining its appearance so far has been missing. It is shown that all characteristic features of Thomas' current echo can be explained resorting to the well known phenomenon of weak localization in disordered systems. In view of recent technological progress in the creation of voltage pulses on a pico-second time scale, the experimental verification of the current echo should be feasible. The echo phenomenon may become a useful tool to determine dephasing rates in weakly disordered systems. The second topic is titled ``Dephasing by Kondo impurities". In this part of the thesis we derive an analytical expression for the dephasing rate of non-interacting electrons propagating in a weakly disordered environment and scattering from very low concentrations of magnetic impurities. The motivation to study dephasing due to Kondo impurities traces back to a series of experiments performed over the last decade which show an unexpected saturation of the dephasing rate at lowest temperatures. The observed saturation clearly deviates from theoretical predictions based on the assumption that inelastic scattering due to electron-electron interactions is the dominant mechanism for dephasing. Therefore, it was suggested, that inelastic scattering from low concentrations of magnetic impurities may be responsible for the observed excess of dephasing. So far these speculations could not be quantitatively tested, since the dephasing rate due to diluted magnetic impurities in the experimentally probed range of temperature was unknown. Based on our results a quantitative comparison between theoretically predicted and experimentally measured dephasing rate was done. This allowed for a critical examination of the relevance of low concentrations of magnetic impurities for the observed behaviour of the dephasing rate. Moreover, this part of the thesis analyzes the magnetic field dependence of the dephasing rate due to magnetic impurities and generalizes the results for the dephasing rate from magnetic to arbitrary diluted dynamical impurities. The third topic of this thesis relates to quantum interference and dephasing in a disordered Luttinger liquid. The standard approach to describe interacting one-dimensional systems is the bosonization method, which, however, becomes very intransparent when in addition disorder comes into play. Therefore, quantum interference phenomena and dephasing in a disordered Luttinger liquid remained unaddressed for a long time. In this part of the thesis we follow a new road and derive an effective field theory for the disordered Luttinger liquid. This model allows to systematically explore interference phenomena in disordered Luttinger liquids. As an application of the model we discuss for the first time the persistent current in a weakly disordered Luttinger liquid