Wind turbine power output short-term forecast : a comparative study of data clustering techniques in a PSO-ANFIS model

Abstract:The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models

Characterising the Quality Journey of Total Quality Management in Relation to the Financial Performance of SMEs Under Crisis Conditions: the Case for Greece

During the last few years Greece is coping with severe economic and financial crisis conditions. Since the Greek SME are the largest productive and economic sector in the Greek economy, they bear the brunt of consequences for these adverse conditions. This thesis investigates the role of the total quality elements in improving or otherwise the financial performance of those SME. Ratio analysis is used as a mean of measuring the SME financial performance and specifically the level of their liquidity, profitability, efficiency and solvency. Furthermore the thesis tries to identify the role that each quality element and quality as a whole, have on different size SME and their financial performance. A data-triangulation methodology was developed to examine the influence of the above factors (use of a questionnaire and a set of semi-structured interviews) and the results and the conclusions derived have shown that:- - All the Greek SME have the intention to continue their quality journey to TQM. - Small SME give more emphasis in implementing the TQM elements, followed by the micro and the medium size SME respectively. However each group of SME prioritises differently the various quality elements. - The ratio analysis revealed that the group with the mostly improved financial performance was the micro SME followed by the small and the medium SME. - The worst financial performance of all the SME occurred between 2008 and 2010. - Amongst the SME that have managed to continue to operate, the ones characterised as TQM SME have shown an improved financial performance. Measuring the SME financial performance, efficiency and solvency were revealed as the most significant variables. The smaller in size the SME were, they pay a greater attention to efficiency while the larger SME pay more attention to solvency. - Utilising the Z-score rate as a criterion, the largest number of transitions among different levels of financial sustainability was revealed from the micro SME. Lower variability was identified from the TQM SME group in comparison with the other two groups of SME (ISO+ and ISO) that have also shown a very similar behaviour. In conclusion SME’s that have followed the ISO to TQM journey during the harsh financial conditions they were facing, they have managed to harbour themselves better in conditions of financial crisis