A new adaptive fuzzy inference system for electricity consumption forecasting with hike in prices

Large increase or hike in energy prices has proven to impact electricity consumption in a way which cannot be drawn from historical data, especially when price elasticity of demand is not significant. This paper proposes an integrated adaptive fuzzy inference system (FIS) to estimate and forecast long-term electricity consumption when prices experience large increase. To this end, first a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Logarithmic linear regressions are estimated with historical data and used to construct an initial first-order TS-FIS. Then, in the adaptation phase, expert knowledge is used to define new fuzzy rules which form a new secondary FIS for electricity forecasting. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual electricity consumption in Iran where removing energy subsidies has resulted in a hike in electricity prices. Gross domestic product (GDP), population and electricity price are three inputs for the initial TS-FIS. A questionnaire survey was conducted to collect the expert estimation on possible change in electricity per capita, change in electricity intensity and the ratio of GDP elasticity to population elasticity when price hikes. Based on the information collected, a fuzzy rule base is formed and used to construct the secondary FIS which is used for electricity forecasting until 2016. Furthermore, the performance of the proposed model of this paper is compared with three other models namely ANFIS, ANN and one-stage regression in terms of their mean absolute percentage error. The comparison shows a superior performance for the proposed FIS model.     

Prediction microhardness profile of functionally graded steels by ANFIS

In the present study, the Vickers microhardness profile of ferritic and austenitic functionally graded steel produced by electroslag remelting process has been modeled by adaptive network-based fuzzy inference system (ANFIS). To produce functionally graded steels, a spot-welded electrode that consists of two slices of plain carbon steel and austenitic stainless steel was used. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with ANFIS. To build the model for graded ferritic and austenitic steels, training, testing and validation using, respectively, 174 and 120 experimental data were conducted. According to the input parameters, the Vickers microhardness of each layer was predicted. The training, testing and validation results in the ANFIS models have shown a strong potential for predicting microhardness profile of both graded ferritic and austenitic steels. It was shown that the Vickers microhardness can be predicted by ANFIS in the range of the examined data.     

Artificial neural network to predict the effect of heat treatments on Vickers microhardness of low-carbon Nb microalloyed steels

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900–1,100°C) and subsequent cooling rate (0.15–227°C/s) and initial austenite grain size (5–130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness.     

A population game approach for dynamic resource allocation problems

We consider a water distribution system as an example of resource allocation, and investigate the use of a population game for its control. We use a game-theoretic approach based on two evolutionary dynamics, the Brown–von Neumann–Nash and the Smith dynamics. We show that the closed-loop feedback interconnection of the water distribution system and the game-theoretic-based controller has a Nash equilibrium as an asymptotically stable equilibrium point. The stability analysis is performed based on passivity concepts and the Lyapunov stability theorem. An additional control subsystem is considered for disturbance rejection. We verify the effectiveness of the method by simulations under different scenarios.     

Impact Energy of Functionally Graded Steels in Crack Arrester Configuration

Charpy impact energy of functionally graded steels produced by electroslag remelting composed of graded ferrite and austenite layers together with bainite or martensite intermediate layer in the form of crack arrester configuration has been investigated. The results obtained in the present study indicate that the notch tip position with respect to bainite or martensite layer significantly affects the impact energy. The closer the notch tip to the tougher layer, the higher the impact energy of the composite due to increment of energy absorbed by plastic deformation zone ahead of the notch and vice versa. Empirical relationships have been determined to correlate the impact energy of functionally graded steels to the morphology of layers.     

XPS and IR study of carbon thin films prepared under negative substrate DC voltage bias

Hydrogenated amorphous carbon (a-C:H) thin films were prepared on glass substrates at different applied DC voltage bias by the HF-CVD method. Other factors of deposition were kept constant. The IR and XPS spectra of the films were obtained. By the deconvolution of the IR and XPS spectra sp³/sp² ratio calculated. The sp³/sp² ratio varies nonlinearly with bias voltage and it has a minimum and maximum in the 0–70 V range of the bias voltage.