GA-ANN Short-Term Electricity Load Forecasting

This paper presents a methodology for short-term load forecasting based on genetic algorithm feature selection and artificial neural network modeling. A feed forward artificial neural network is used to model the 24-h ahead load based on past consumption, weather and stock index data. A genetic algorithm is used in order to find the best subset of variables for modeling. Three data sets of different geographical locations, encompassing areas of different dimensions with distinct load profiles are used in order to evaluate the methodology. The developed approach was found to generate models achieving a minimum mean average percentage error under 2 %. The feature selection algorithm was able to significantly reduce the number of used features and increase the accuracy of the models.

Spatial distribution patterns of the non-native European catfish, Silurus glanis, from multiple online sources - a case study for the River Tagus (Iberian Peninsula)

Effective management of invasive fishes depends on the availability of updated information about their distribution and spatial dispersion. Forensic analysis was performed using online and published data on the European catfish, Silurus glanis L., a recent invader in the Tagus catchment (Iberian Peninsula). Eighty records were obtained mainly from anglers’ fora and blogs, and more recently from www.youtube.com. Since the first record in 1998, S. glanis expanded its geographic range by 700 km of river network, occurring mainly in reservoirs and in high-order reaches. Human-mediated and natural dispersal events were identified, with the former occurring during the first years of invasion and involving movements of >50 km. Downstream dispersal directionality was predominant. The analysis of online data from anglers was found to provide useful information on the distribution and dispersal patterns of this non-native fish, and is potentially applicable as a preliminary, exploratory assessment tool for other non-native fishes.