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Load estimation based on self-organizing maps and bayesian networks for microgrid design in rural zones

Microgrids are suitable electrical solutions for providing energy in rural zones. However, it is challenging to propose in advance a good design of the microgrid because the electrical load is difficult to estimate due to its highly dependence of the residential consumption. In this paper, a novel estimation methodology for the residential load profiles is proposed. Socio-demographic data and electrical power consumption are used to generate significant knowledge about the load behavior. Socio-demographic data are used as input for a neural network called Self-Organizing Maps (SOM). The SOM proposes a way to group dwelling according to their different features. Moreover, a probabilistic model based on Bayesian networks incorporates daily variations of the electrical load, simulating the behavior of the electrical appliances. The methodology, as a whole, is applied to a case study in a rural community located in Chile. The methodology is easily adaptable to other rural communities.Railway Engineerin

Load estimation for microgrid planning based on a self-organizing map methodology

This study presents a novel load estimation method for isolated communities that do not receive energy or only receive it for a limited time each day. These profiles have been used to determine the installed capacity of generating units for microgrid electrification projects. The social characteristics and lifestyles of isolated communities differ from those in urban areas; therefore, the load profiles of microgrids are sensitive to minor variations in generation and/or consumption. The proposed methodology for obtaining the residential profiles is based on clustering algorithms such as k-means, a self-organizing map (SOM) or others. In this work, SOM clustering is considered because it allows a better interpretation of results that can be contrasted with social aspects. The proposed methodology includes the following components. First, the inputs are processed based on surveys of residents that live in each socio-economic level of housing and the community. Second, family types are clustered using an SOM, from which relevant information is derived that distinguishes one family from another. Third, the load profiles of each cluster are selected from a database. Additionally, social aspects and relevant energy supply information from communities with similar characteristics are used to generate the required database. The SOM for the clustering of families of the community with available energy measurements is used as an initial guess for the clustering of the families in the community with unknown energy measurements. The methodology is applied and tested in the community of El Romeral, Chile, where a microgrid will be installed. The SOM technique compares favorably with a benchmark method that uses the average load profile of a community; furthermore, the SOM clustering algorithm for the methodology is favorably compared with the k-means algorithm because the results obtained by SOM are consistent with the social aspects.Railway Engineerin