ESTIMATION OF UNBALANCE COST DUE TO DEMAND PREDICTION ERRORS USING ARTIFICIAL NEURAL NETWORK

Estimation of energy demand is used as an important tool for decision makers determining company strategies and policies. Apart from this, the fact that the actual consumption differs from the forecast is harmful for the economy of the company and even for the economy of the big scale. In this study, it is aimed to estimate the imbalance aberration caused by demand forecast deviation with Artificial Neural Networks and to evaluate its results

Artificial Neural Network Models for Predicting the Energy Consumption of the Process of Crystallization Syrup in Konya Sugar Factory

In this study, a model has been developed from the sugar production process stages in Konya Sugar Factory using artificial neural networks to estimate the energy consumption of the process of crystallization syrup. Model developing specific enthalpy, mass and pressure as input layer parameters and consumption energy as output layer was used. 124 different data are taken from Konya Sugar Factory during January 2016. Feed-forward backpropagation algorithm was used in the training phase of the network. Learning function LEARNGDM and the number of hidden layer kept constant as 2 and transfer functions are modified. To find the most optimal model, 27 artificial neural networks with different architectures have been tested. 2-5-1 network architecture was determined as the best suitable network architecture and transfer function is determined logsig function as the optimal transfer function. Optimum results of the model taken in the coefficient of determination was found R 0.98 neural network training, testing and validate was also found to be R 0.98, the performance of the network for not shown data to network was found R0.9

The Effects of Natural Zeolit Supplemented into Litter on Growth Performance and Welfare of Broilers

The objective of this study was to assess the effect of clinoptiolite (a natural zeolite) in two addition level (3 kg or 6 kg) and two particle size (ranging from 0.1 to 0.2 mm or 0.5 to 1.0 mm) as litter supplement on the growth performance, litter dry matter, ammonium release and foot pad lesion score of broilers. One thousand and eight hundred, one-day-old chickens were divided into five treatment groups with six replicates of 60 chicks. Bird density was 15/ m2 in floor pens where pine shavings was used as litter at the level of 5 kg/m2. Considering the main effect, neither addition level nor the particle size of zeolite supplemented to litter influenced the performance indices and mortality of broilers during the starter (1 to 28 days) and overall growth period (28 to 42 days). Supplementing zeolite to litter at the level of 6 kg/m2 induced a significant increase in litter dry matter (%) at days 28 and 35 when compared to treatment with 3 and 0 kg/m2. At 42 days of age, food pad lesion score of birds reared on litter supplemented with large sized zeolite particles was lower than those grown on litter with added little sized zeolite. In conclusion, addition level and particle size of natural zeolite supplemented to litter had no significant effect on broiler performance and ammonia release from litter