During the decades the district heating's (DH) advantages (more cost-efficient heat generation and reduced air pollution) overcompensated the additional costs of transmission and distribution of the centrally produced thermal energy to consumers. Rapid increase in the efficiency of low-power heaters, development of separated low heat density areas in cities reduce the competitiveness of the large centralized DH systems in comparison with the distributed cluster-size networks and even local heating. Reduction of transmission costs, enhancement of the network efficiency by optimization of the design of the DH networks become a critical issue. The methodology for determination of the key drivers of the cost-efficiency of the DH networks to implement the most efficient (cost-minimal) thermal energy transmission was developed in this study. An inductive benchmarking modelling was applied; the general causal regularity is based on the observations of specific cases, thus determining the relationships between the network's design and thermal indicators as predictors and transmission costs as the target variable. The key drivers of the network efficiency were disclosedthe network length and the largest inner diameter of the pipes. The methodology is applicable for use by municipalities and heat providers for the heating planning of the new housing developments as well as renovation and/or expansion of the existing DH networks.
Abstract.A data warehouse typically is a collection of historical data designed for decision support, so it is updated from the sources periodically, mostly on a daily basis. Today's business however asks for fresher data. Real-time warehousing is one of the trends to accomplish this, but there are a number of challenges to move towards true real-time. This paper proposes 'Multi-stage Trickle & flip' methodology for data warehouse refreshment. It is based on the 'Trickle & flip' principle and extended in order to further insulate loading and querying activities, thus enabling both of them to be more efficient.
This paper examines emotion intensity prediction in dialogs between clients and customer support representatives occurring on Twitter. We focus on a single emotion type, namely, frustration, modelling the user's level of frustration while attempting to predict frustration intensity on the current and next turn, based on the text of turns coming from both dialog participants. A subset of the Kaggle Customer Support on Twitter dataset was used as the modelling data, annotated with per-turn frustration intensity ratings. We propose to represent dialog turns by binary encoded bags of automatically selected keywords to be subsequently used in a machine learning classifier. To assess the classification quality, we examined two different levels of accuracy imprecision tolerance. Our model achieved a level of accuracy significantly higher than a statistical baseline for prediction of frustration intensity for a current turn. However, we did not find the additional information from customer support turns to help predict frustration intensity of the next turn, and the reason for that is possibly the stability of user’s frustration level over the course of the conversation, in other words, the inability of support’s response to exert much influence to user’s initial frustration level.
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