## 1 Introduction  
The energy business is going through a series of swift and radical transformations to meet the growing demands for sustainable energy. The future of the energy sector will, to a large extent, be formed by a transformation in the electricity sector, posing challenges for traditional electrical power systems. This shift is of a complex nature, but offers ample opportunities for business and information analytics to support the transition [1]. The electricity grid faces decentralized production from renewable sources, electric mobility, and related advances. These are at odds with traditional power systems, where central large-scale generation of electricity follows inelastic consumer demand. Additionally, renewable electricity sources are, unlike traditional sources, volatile in nature and strongly dependent on external factors such as weather conditions, making short-term energy production forecasting increasingly important [2]. The non-storability and volatile aspect of sustainable energy sources and the required shift from a demand-driven to a supply-driven market means the energy transition requires system flexibility from all market participants. On the retail side, end-consumers can offer demand flexibility to the grid by shifting their load on moments of peak demand to other times in the day. Energy services and utilities can incentivize such behavior [3], by optimizing feedback to their customers and enabling new forms of dynamic trading. Energy Management Systems (EMS) can control the use of end-appliances and optimize the flexible range, based upon the end consumer's personal preferences and beliefs [4]. Demand Response (DR) programs offer financial incentives to take action to reduce or shift load in correspondence to market price behavior. As such, DR builds upon the behavioral traits of energy consumers via comprehensive communication through home energy systems. The effectiveness of such programs is largely affected by the willingness of end-users to be involved in such programs [5]. Engaging customers requires systematic communication and interaction between the utility provider and the people it serves, with the intent of building trust, respect, and achieving optimal energy usage amongst the heterogeneity of individual households. The heterogeneity amongst customers ensures that EMS and the energy service provider have to learn from the customer's individual preferences in order to make optimal recommendations in terms of dynamic tariff targeting, and ultimately, consumer welfare. We apply machine learning techniques in this paper, to show how such systems can recommend tariff schemes based upon the individual household's attributes. Our setting is a natural experiment, involving real-world customers of a national utility company participating via dynamic pricing contracts. This allows us to reach high ecological validity in testing, whether the increase of household informedness influences demand for individual households, and how utilities can use machine learning techniques in designing their home energy management systems for better targeting of customers. With utility providers starting to introduce dynamic price tariff schemes to residential households, demand side flexibility may enhance stabilizing the grid load under the right price incentives [6]. Demand side management strategies, such as demand response, can have a dual effect of reducing electricity consumption and allowing greater efficiency and flexibility in grid management [7]. Demand response refers to a wide range of actions that can be taken on the customer side of the electricity meter, in order to respond to specific conditions within the electricity system [8]. The different DR programs can generally be classified into two main categories, namely Incentive-Based Programs (IBP) and Price-based Programs (PBP) [9]. In PBP programs, consumers are offered dynamic pricing rates over time, typically with prices significantly higher during peak-periods than during off-peak periods.