Towards An Enhanced Backpropagation Network for Short-Term Load Demand Forecasting

Artificial neural networks (ANNs) are ideal for the prediction and classification of non-linear relationships however they are also known for computational intensity and long training times especially when large data sets are used. A two-tiered approach combining data mining algorithms is proposed in order to enhance an artificial neural network’s performance when applied to a phenomenon exhibits predictable changes every calendar year such as that of electrical load demand. This approach is simulated using the French zonal load data for 2016 and 2017. The first tier performs clustering into seasons and classification into day-types. The second tier uses artificial neural networks to forecast 24-hour loads. The first tier results are the focus of this. The K-means algorithm is first applied to the morning slope feature of the data set and a comparison is then made between the Naïve Bayes algorithm and the k-Nearest Neighbors algorithm to determine the better classifier for this particular data set. The first tier results show that calendar-based clustering does not accurately reflect electrical load behavior. The results also show that k-Nearest Neighbors is the better classifier for this particular data set. It is expected that by optimizing the data set and reducing training time, the learning performance of ANN-based short-term load demand forecasting

URBAN MAP GENERATION IN ARTIST’S STYLE USING GENERATIVE ADVERSARIAL NETWORKS (GAN)

Artificial Intelligence is a field that is able to learn from existing data to synthesize new ones using deep learning methods. Using Artificial Neural Networks that process big datasets, complex tasks and challenges become easily resolved. As the zeitgeist suggests, it is possible to produce novel outcomes for future projections by applying various machine learning algorithms on the generated data sets. In that context, the focus of this research is exploring the reinterpretation of 21st century urban plans with familiar artist styles using different subtypes of deep-learning-based generative adversarial networks (GAN) algorithms. In order to explore the capabilities of urban map transformation with machine learning approaches, two different GAN algorithms which are cycleGAN and styleGAN have been applied on the two main data sets. First data set, the urban data set, contains 50 cities urban plans in .jpeg format collected according to the diversity of the urban morphologies. Whereas the second data set is composed of four well-known artist’s paintings, that belong to various artistic movements. As a result of training the same data sets with different GAN algorithms and epoch values were compared and evaluated. In this respect, the study not only investigates the reinterpretation of stylistic urban maps and shows the discoverability of new representation techniques, but also offers a comparison of the use of different image to image translation GAN algorithms

Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. In this paper, we propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1st and 2nd order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a recently developed cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity