In this paper, we analyze a public dataset of electricity consumption collected over 3,800 households for one year and half. We show that some socio-economic factors are critical indicators to forecast households' daily peak (and total) load. By using a random forests model, we show that the daily load can be predicted accurately at a fine temporal granularity. Differently from many state-of-the-art techniques based on support vector machines, our model allows to derive a set of heuristic rules that are highly interpretable and easy to fuse with human experts domain knowledge. Lastly, we quantify the different importance of each socio-economic feature in the prediction task.

Media marketers and researchers have shown great interest in what becomes a trend within social media sites. Their interests have focused on analyzing the items that become trends, and done so in the context of Youtube, Twitter, and Foursquare. Here we move away from these three platforms and consider a new mobile social-networking application with which users share pictures of "cool" things they find in the real-world. Besides, we shift focus from items to people. Specifically, we focus on those who generate trends (trend makers) and those who spread them (trend spotters). We analyze the complete dataset of user interactions, and characterize trend makers (spotters) by activity, geographical, and demographic features. We find that there are key characteristics that distinguish them from typical users. Also, we provide statistical models that accurately identify who is a trend maker (spotter). These contributions not only expand current studies on trends in social media but also promise to inform the design of recommender systems, and new products.

Social media sites have used recommender systems to suggest items users might like but are not already familiar with. These items are typically movies, books, pictures, or songs. Here we consider an alternative class of items - pictures posted by design-conscious individuals. We do so in the context of a mobile application in which users find "cool" items in the real world, take pictures of them, and share those pictures online. In this context, temporal dynamics matter, and users would greatly profit from ways of identifying the latest design trends. We propose a new way of recommending trending pictures to users, which unfolds in three steps. First, two types of users are identified - those who are good at uploading trends (trend makers) and those who are experienced in discovering trends (trend spotters). Second, based on what those "special few" have uploaded and rated, trends are identified early on. Third, trends are recommended using existing algorithms. Upon the complete longitudinal dataset of the mobile application, we compare our approach's performance to a traditional recommender system's.