A Survey on Demand-Response in HEMS: Algorithm Types, Objectives and Constraints

Sun, Mingxu; Anaadumba, Raphael; Liu, Qi; Liu, Xiaodong; Yang, Yi

doi:10.1109/smartworld-uic-atc-scalcom-iop-sci.2019.00095

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the literature, a variety of different system architectures and control methods are proposed to achieve the different energy management goals, ranging from simple rule-based controls to generic algorithms, swarm optimization, and model predictive controls [4,5]. HEMSs with basic rule-based controllers might, for example, attempt to maximize the self-consumption of photovoltaic power by feeding excess photovoltaic power into the battery during the day until it is full or drawing power from the battery at night until it is empty.…”

Section: Introductionmentioning

confidence: 99%

Predictive Control of a Real Residential Heating System with Short-Term Solar Power Forecast

Villegas Mier,

Dittmann,

Herzberg

et al. 2023

Energies

View full text Add to dashboard Cite

Predictive control has great potential in the home energy management domain. However, such controls need reliable predictions of the system dynamics as well as energy consumption and generation, and the actual implementation in the real system is associated with many challenges. This paper presents the implementation of predictive controls for a heat pump with thermal storage in a real single-family house with a photovoltaic rooftop system. The predictive controls make use of a novel cloud camera-based short-term solar energy prediction and an intraday prediction system that includes additional data sources. In addition, machine learning methods were used to model the dynamics of the heating system and predict loads using extensive measured data. The results of the real and simulated operation will be presented.

show abstract

Section: Introductionmentioning

confidence: 99%