Electrical Energy Systems, Eindhoven University of Technology, P.O. Box 513, 5600 MB Eindhoven, The Netherlands; [EMAIL_ADDRESS] (P.H.N. ); [EMAIL_ADDRESS] (J.M. ); [EMAIL_ADDRESS] (J.G.S.) Electrical Energy Systems, Eindhoven University of Technology, P.O. Box 513, 56000 MB Eindhoven, The Netherlands; [EMAIL_ADDRESS] (P.H.N. ); [EMAIL_ADDRESS] (J.  
is typically done using a so-called baseline, which describes the behaviour of an asset in case no DR service would have been provided. Baselining is identified as one of the main barriers in the deployment and evaluation of DR. According to [3], accurate baselines are needed in order to enable DR in Europe. To achieve this, the availability of measurements and historical baselines is required. A more in-depth discussion of baselining can be found in [4]. A baseline forms a synthetic and hypothesised profile, and therefore per definition has an error. This observation is seen as a threat to consumer's participation to a DR program, as the (financial) compensation of flexibility provided depends on the accuracy of the baseline. It is furthermore argued that although baselining is already applied for (large) industrial and commercial loads, providing an accurate baseline for smaller devices with irregular consumption might be a challenge [4]. The work of [5] takes the transmission system perspective of deploying flexibility, analysing operational planning, operations, and settlement. As part of their analysis, [5] identifies the lack of an appropriate baselining methodology as the most urgent barrier for aggregators to provide balancing services. Numerous authors have pointed out the necessity of truth-telling in DR schemes. [PERSON] analysed a DR program from a game theoretical perspective [6]. The DR program is tested for its truth-telling and cheat-proof behaviour. The authors show the need for truth-telling in a distributed solution, with consumers behaving naturally selfish. The universal smart energy framework (USEF) foundation identifies gaming as one of the issues with their (self-reported) baseline model, in which deviations from the reported baseline are not penalised [7; 8]. In [9], a DR program is implemented, taking the system operator's perspective. Consumers report their baseline and to ensure truth-telling, consumers deviating from the baseline--while not providing promised flexibility--are penalised with a random penalty. An alternative implementation of this DR program uses a reported baseline in which consumers not only provide their baseline, but also their marginal utility [10]. The authors showed that by adding the consumer's marginal utility, prices for penalty and reward for flexibility delivery vary over time. In the USA, baselining has been applied for over a decade already. In 2008, a study aiming at standardising baseline methodologies has been conducted. In this study, various models are compared based on a statistical analysis of their performance [11]. The five different methodologies defined by the USA's energy standardisation board are presented in [3; 12]. Both conclude that multiple methodologies are required, as no one-size-fits-all solution is available. Every case is individually analysed, and the most suitable baselining methodology is selected. An alternative approach is presented by [13; 14]. Here, a regression-based baseline model has been developed, also focusing on the USA perspective in relation to large industrial and commercial loads. More recently, refs. [15; 16] focuses on residential loads, and ref. [16] focuses on the European context. In [15], it is found that improving the baseline accuracy and reducing the bias does not necessarily result in improved economic benefits of a model. The authors assess the total stakeholders' profits for five different baseline models. It is found that baseline models with a bias positive to the customers result in higher customer participation. [PERSON] [16] proposes a statistical method of control group selection, which eliminates the need for historical datasets.