A Data-Driven Approach for Targeting Residential Customers for Energy Efficiency Programs

The Contribution of Bottom-Up Energy Models to Support Policy Design of Electricity End-Use Efficiency for Residential Buildings and the Residential Sector: A Systematic Review

“…The study concludes that change on factors depends on the country and its specific-energy policy. On the other hand, Liang et al [54] create a model to evaluate domestic appliances' constant power (i.e., baseload). Sliding Window Linear Regression is used to find consistent power-consuming segments and Kernel Density to improve baseload discovery accuracy.…”

“…In contrast, single-sector bottom-up energy models with project coverage are short time horizon models that rely on granular time split with minute resolution. Two studies use the Pecan Street dataset to analyze household baseload consumption [54] and model appliances benchmark [47]. Likewise, Mohseni et al [43] use smart meter samples to create an energy model for day-ahead planning.…”

Mobile Apps Meet the Smart Energy Grid: A Survey on Consumer Engagement and Machine Learning Applications

“…Other machine learning models can also be applied in smart grid mobile apps for consumer profiling. In [75] for example, a linear regression-based algorithm that detects the base load of households is proposed, i.e. constant consumption from always-on appliances like refrigerators/freezers.…”

Machine learning based disaggregation of air‐conditioning loads using smart meter data

“…In this study, the dataset provided by Pecan street Inc. [25], which includes the total electrical load along with appliance‐by‐appliance measurements (including air‐conditioner) in several residential buildings, is employed. Furthermore, buildings with an integrated PV unit have been chosen; thus, the PV plant's generation at each time‐stamp (which represents the solar irradiation) is also available.…”