Claims about the benefits of heating controls are often biased, unsubstantiated, misleading,
or incorrect. This paper presents a systematic and critical international review of the …

This paper proposes a novel scalable type of multi-agent reinforcement learning-based
coordination for distributed residential energy. Cooperating agents learn to control the …

Heating, ventilation, and air-conditioning (HVAC) systems are sized using design conditions,
conditions that occur only a small percentage of time each year. As a result, HVAC systems …

In this paper, we present a first-time cloud-based eco-feedback and gaming platform that
aims to promote energy conserving thermostat-adjustment behaviors in multi-unit residential …

Programmable thermostats with frequent schedule overrides (ie,“holds”) are widely
assumed to be detrimental to potential energy savings. We explored whether this …

Smart-home technologies have been heralded as an important way to increase energy
conservation. While in vitro engineering estimates provide broad optimism, little has been …

Energy providers still struggle to decide on which digital energy services can help them stay
competitive and aligned with energy efficiency policy targets. This research aims to assist …

Commercial office buildings contribute 17 percent of Carbon Emissions in the US, according
to the US Energy Information Administration (EIA), and improving their efficiency will reduce …

With UK housing contributing almost one-third of national final energy consumption, energy
research and policy are striving to understand how residents perceive and use domestic …

This paper investigates the use of deep multi-agent reinforcement learning (MARL) for the
coordination of residential energy flexibility. Particularly, we focus on achieving cooperation …