Evaluation of clustering techniques for generating household energy consumption patterns in a developing country

This work compares and evaluates clustering techniques for generating representative daily load profiles that are characteristic of residential energy consumers in South Africa. The input data captures two decades of metered household consumption, covering 14 945 household years and 3 295 848 daily load patterns of a population with high variability across temporal, geographic, social and economic dimensions. Different algorithms, normalisation and pre-binning techniques are evaluated to determine the best clustering structure. The study shows that normalisation is essential for producing good clusters. Specifically, unit norm produces more usable and more expressive clusters than the zero-one scaler, which is the most common method of normalisation used in the domain. While pre-binning improves clustering results for the dataset, the choice of pre-binning method does not significantly impact the quality of clusters produced. Data representation and especially the inclusion or removal of zero-valued profiles is an important consideration in relation to the pre-binning approach selected. Like several previous studies, the k-means algorithm produces the best results. Introducing a qualitative evaluation framework facilitated the evaluation process and helped identify a top clustering structure that is significantly more useable than those that would have been selected based on quantitative metrics alone. The approach demonstrates how explicitly defined qualitative evaluation measures can aid in selecting a clustering structure that is more likely to have real world application. To our knowledge this is the first work that uses cluster analysis to generate customer archetypes from representative daily load profiles in a highly variable, developing country contex

Fuzzy expert systems in civil engineering

Imperial Users onl

Profitability Evaluation and Ranking of Indian Non-Life Insurance Firms using GRA and TOPSIS

This paper evaluates the profitability of the Indian non-life insurance firms in the period 2008-2013 using multi criteria decision analysis methods :GRA and TOPSIS based on the profitability ratios. Also, ranking of the non-life insurance firms is arrived. Few studies on efficiency of Indian non-life insurance firms using different DEA models were studied, but the number of inputs and outputs considered are very few as the DEA convention doesn't allow number of DMUs more than three times the sum of inputs and outputs. But, the profitability evaluation involves more number of decision variables considered in efficiency studies using DEA models. This paper addresses this gap by evaluating the profitability of the alternative non-life firms with more number of decision variables or criteria using Multi-Criteria Decision Analysis: GRA and TOPSIS Keywords: Non-life Insurance, Profitability, GRA,TOPSI