The development of accurate models for load forecast is becoming increasingly important, following the steady electrification of domestic demand and the growing penetration of renewable generation. To this end, this work describes all the steps required for a complete energy baseline estimation, from data retrieval and exploratory analysis to the the development and testing of the actual predicting models. Specifically, we present (i) a multi-variable linear regression model which predicts the baseline energy consumption based on several independent variables, (ii) a uni-variate linear regression model with time series using the lag (time-shift) feature and, (iii) a deep learning Convolutional Neural Network (CNN) model. The root mean squared error has been chosen as the main metric to compare the performance of each model. Results show that the energy demand forecast provided by the CNN model outperforms the linear regression models as the former is able to accurately capture the spatio-temporal dependencies in buildings' load profiles, making it a suitable deep learning model for load forecast problems.

Explicit demand response plays a significant role in the future energy grid transition, as it involves end consumers in smart grid activities and, at the same time, exploits the potential of flexibility, giving the opportunity to grid operators to accommodate a total amount of energy without the need to reinforce the grid infrastructure. For evaluating the successfulness of a demand response program, thus, evaluating its advantages, it is fundamental to have an accurate baseline curve consumption along with meaningful key performance indicators. In this work, we propose a novel way of calculating the baseline consumption using artificial intelligence techniques. In particular, regression models have been applied to a database of historical data. In order to present a complete evaluation of demand response programs, we present five key performance indicators (KPIs). The KPIs have been selected so as to depict the successfulness of the explicit demand response program. We suggest a novel way of evaluating two of the five KPI using a quantitative approach. We also apply the proposed methodology for baseline calculation and KPIs evaluation in a practical example: two pilot sites have been used and real-life scenarios of demand response events have been applied for this scope to actual nonindustrial consumers and especially residential consumers. The baseline has been calculated for these pilot sites and the KPIs have been evaluated for them. The presented results complete the picture of evaluating a real-life demand response program and show the effectiveness of the selected approach. The proposed schemes for baseline calculation and KPI evaluation can be used by the scientific community for evaluating future demand response programs, especially in the residential sector.