NETNOMICS Economic Research and Electronic Networking

R. Tierney, Heather; Pan, Bing

doi: 10.1007/s11066-013-9072-xpmid: N/A

A new area of research involves the use of normalized and scaled Google search volume data to predict economic activity. This new source of data holds both many advantages as well as disadvantages. Daily and weekly data are employed to show the effect of aggregation in Google data, which can lead to contradictory findings. In this paper, Poisson regressions are used to explore the relationship between the online traffic to a specific website and the search volumes for certain search queries, along with the rankings of that website for those queries. The purpose of this paper is to point out the benefits and the pitfalls of a potential new source of data that lacks transparency in regards to the raw data, which is due to the normalization and scaling procedures utilized by Google.

Cadre, Hélène; Mercier, David

doi: 10.1007/s11066-013-9075-7pmid: N/A

In this article, we consider houses belonging to an eco-neighborhood in which inhabitants have the capacity to optimize dynamically the energy demand and the energy storage level so as to maximize their utility. The inhabitants’ preferences are characterized by their sensitivity toward comfort versus price, the optimal expected temperature in the house, thermal loss and heating efficiency of their house. At his level, the eco-neighborhood manager shares the resource produced by the eco-neighborhood according to two schemes: an equal allocation between the houses and a priority based one. The problem is modeled as a stochastic game and solved using stochastic dynamic programming. We simulate the energy consumption of the eco-neighborhood under various pricing mechanisms: flat rate, peak and off-peak hour, blue/white/red day, peak day clearing and a dynamic update of the price based on the consumption of the eco-neighborhood. We observe that economic incentives for houses to store energy depend deeply on the implemented pricing mechanism and on the homogeneity in the houses’ characteristics. Furthermore, when prices are based on the consumption of the eco-neighborhood, storage appears as a compensation for the errors made by the service provider in the prediction of the consumption of the eco-neighborhood.