Technoeconomic Projections with Artificial Neural Networks using an Ensemble of Sparsely-Sampled Bootstrapped Data

Abstract

Aims: The present study refers to developing an artificial neural network (ANN) that can be designed exclusively for ex ante forecasting in technoeconomic contexts using an ensemble set of sparse and insufficient sampled-data availed ex post. Study Design: In general, the samples in a data set of technoeconomic structures would largely be limited in number due to sparse-sampling; also, availability of number of such sets is mostly inadequate for robust training of an ANN so as to obtain realistic inferences subsequently in the prediction phase. Hence, a sparsity-recovery strategy is advocated via a cardinality enhancement procedure (through Nyquist sampling) performed on the sparse data set in order to augment the number of samples in its sampled-data space. Further, the concept of statistical bootstrapping technique of resampling is invoked and applied on the cardinality-improved subset so as to obtain an enhanced number of data sets. This ensemble of data set is then adopted to facilitate robust training of the test Original Research Article British Journal of Economics, Management & Trade, 4(2): 228-263, 2014 229 ANN. Place and Duration of Study: The studies were conducted (2012-2013) at: Department of Computer and Electrical Engineering and Computer Science, College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, Florida 33431, USA. Methodology: The study governs technoeconomic ex ante projections pertinent to a wind-power generation business complex elucidated via ANN-based forecasting. Relevant test ANN is designed to accommodate training with an ensemble of sampled set available ex post but, in limited numbers. The associated scarcity is recovered by artificially enhancing the data space to an adequate extent via Nyquist sampling and bootstrapping techniques. Further, the test ANN designed corresponds to a multilayer perceptron (MLP) supporting backpropagation of the perceived error at the output with respect to a supervisory value. It accommodates the bootstrapped data space at its input relevant to technoeconomic details on a practical wind-power system performance reported in the literature. The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered. Results: Using the test ANN trained with bootstrap-enhanced, scarcity-recovered sparse data on wind-power generation statistics and associated plant economics, reliable inference (in the prediction phase) is achieved on the system performance. That is, the ANN output obtained depicts forecast projections on the productivity of electric power generation in the ex ante regime. Simulation studies thereof and results obtained demonstrate the efficacy of the method proposed, bootstrapping algorithm developed and the use of MLP in the technoeconomic contexts.