Classical energy planning models assume that consumers are rational, which is obviously rarely the case. This paper proposes an original method to take into account the consumer's real behavior in an energy model. This new hybrid model combines technical methods from operations research with behavioral approaches from social sciences and couples a classical energy model with a Share of Choice model
Although in 2012 the European Union (EU) has promoted energy efficiency in order to ensure a gradual 20% reduction of energy consumption by 2020, its targets related to energy efficiency have increased and extended to new time horizons. Therefore, in 2016, a new proposal for 2030 of energy efficiency target of 30% has been agreed. However, during the last years, even if the electricity consumption by households decreased in the EU-28, the largest expansion was recorded in Romania. Taking into account that the projected consumption peak is increasing and energy consumption management for residential activities is an important measure for energy efficiency improvement since its ratio from total consumption can be around 25-30%, in this paper, we propose an informatics solution that assists both electricity suppliers/grid operators and consumers. It includes three models for electricity consumption optimization, profiles, clustering and forecast. By this solution, the daily operation of appliances can be optimized and scheduled to minimize the consumption peak and reduce the stress on the grid. For optimization purpose, we propose three algorithms for shifting the operation of the programmable appliances from peak to off-peak hours. This approach enables the supplier to apply attractive time-of-use tariffs due to the fact that by flattening the consumption peak, it becomes more predictable, and thus improves the strategies on the electricity markets. According to the results of the optimization process, we compare the proposed algorithms emphasizing the benefits. For building consumption profiles, we develop a clustering algorithm based on self-organizing maps. By running the algorithm for three scenarios, well-delimited profiles are obtained. As for the consumption forecast, highly accurate feedforward artificial neural networks algorithm with backpropagation is implemented. Finally, we test these algorithms using several datasets showing their performance and integrate them into a web-service informatics solution as a prototype.
Abstract. In recent decades the supply perspective of tourism, focusing on large agglomerations of tourism companies that bring benefits in terms of positive externalities at destination, has been more and more emphasized. It has become a complement of the classical demand-based perspective, which points to the availability of resources (attractions) demanded by tourists as the exclusive explanation for the location decisions of tourist companies. In line with these new orientations, our paper proposes an inquiry into the spatial distribution of accommodation and foodservice companies in Romania, seeking to reveal whether a significant cross-correlation between these two segments of tourism infrastructure occurs and, in case of an affirmative answer, to discuss their significance for tourism development policies. With this aim in view, the investigation methodology utilises a series of analytical tools that combine GIS and spatial agglomeration analysis based techniques, applied to datasets capturing all companies represented in the tourism industry in Romania provided by the National Authority for Tourism, combined with spatial data from the Environmental Systems Research Institute (ESRI). The results indicate an uneven territorial distribution of tourism infrastructure compared to the location of tourist attractions, significant differences between the geographical distribution of the accommodation and foodservice companies and suggest differentiated policies for supporting tourism infrastructure, in accordance with the specific needs of the tourist areas.JEL classification: C19, C88, L83, R12
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