The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water (DHW) production in residential buildings. This is done by deriving data-driven optimal heating schedules (used interchangeably with policies) automatically. The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism -an air-source heat pump (ASHP) in this case -at the highest possible efficiency. The proposed algorithm, while tested on an ASHP, is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well. Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings (NZEB) in the Netherlands are presented. These show a reduction of energy consumption for DHW by 20% using a computationally inexpensive heuristic approach, and 27% when using a more intensive hybrid ant colony optimization based method. The energy savings are strongly dependent on occupant comfort. This is demonstrated in real world settings for a low-consumption house where active control was performed using heuristics for 3.5 months and resulted in energy savings of 27% (61 kWh). It is straightforward to extend the same models to perform automatic demand side management (ADSM) by treating the DHW vessel as a flexibility bearing device.

Energy consumption for hot water production is a major draw in high efficiency buildings. Optimizing this has typically been approached from a thermodynamics perspective, decoupled from occupant influence. Furthermore, optimization usually presupposes existence of a detailed dynamics model for the hot water system. These assumptions lead to suboptimal energy efficiency in the real world. In this paper, we present a novel reinforcement learning based methodology which optimizes hot water production. The proposed methodology is completely generalizable, and does not require an offline step or human domain knowledge to build a model for the hot water vessel or the heating element. Occupant preferences too are learnt on the fly. The proposed system is applied to a set of 32 houses in the Netherlands where it reduces energy consumption for hot water production by roughly 20% with no loss of occupant comfort. Extrapolating, this translates to absolute savings of roughly 200 kWh for a single household on an annual basis. This performance can be replicated to any domestic hot water system and optimization objective, given that the fairly minimal requirements on sensor data are met. With millions of hot water systems operational worldwide, the proposed framework has the potential to reduce energy consumption in existing and new systems on a multi Gigawatt-hour scale in the years to come.

Increasing energy efficiency of thermostatically controlled loads has the potential to substantially reduce domestic energy demand. However, optimizing the efficiency of thermostatically controlled loads requires either an existing model or detailed data from sensors to learn it online. Often, neither is practical because of real-world constraints. In this paper, we demonstrate that this problem can benefit greatly from multi-agent learning and collaboration. Starting with no thermostatically controlled load specific information, the multi-agent modelling and control framework is evaluated over an entire year of operation in a large scale pilot in The Netherlands, constituting over 50 houses, resulting in energy savings of almost 200 kWh per household (or 20% of the energy required for hot water production). Theoretically, these savings can be even higher, a result also validated using simulations. In these experiments, model accuracy in the multi-agent frameworks scales linearly with the number of agents and provides compelling evidence for increased agency as an alternative to additional sensing, domain knowledge or data gathering time. In fact, multi-agent systems can accelerate learning of a thermostatically controlled load's behaviour by multiple orders of magnitude over single-agent systems, enabling active control faster. These findings hold even when learning is carried out in a distributed manner to address privacy issues arising from multi-agent cooperation.

Over the last decade, supply-side constraints have resulted in widespread electricity shortage in Pakistan. At its peak, this amounted to over a 7 GW supply-demand gap and caused the electricity grid to be offline for vast swathes of population for many hours daily. Despite major supply-side investments acute shortages persist and a large percentage of relatively affluent households, estimated in millions, have countered this by investing in self-generation and battery storage technologies (usually lead-acid batteries because of their low cost). This paper summarizes the impact of this backup technology on the broader energy system in terms of efficiency losses for households and contribution to low-voltage grid congestion. Research findings suggest that the low efficiency of these backup systems has caused annual losses of around 3-4 TWh for the electric grid in Pakistan as well as overloading of transformers and frequent supply-demand imbalances. However, the mass adoption of these backup systems has also created an entire ecosystem which can enable massive demand side management and provide the framework for a future smart grid in Pakistan. Besides evaluating the opportunities, possible policy measures the government should undertake to enable this transition are also discussed.

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With increasing proliferation levels of variable energy sources, flexible energy loads will become increasingly important to help stabilize the energy grid. Increasing electrification of heating systems means that the thermal inertia of buildings and hot water vessels can provide a ubiquitous, low cost alternative to electrical storage for providing this energy flexibility. However, it is unclear how this flexibility fluctuates with various factors such as occupant behaviour and ambient conditions in the real world. This paper quantifies the effect of different influencing factors on the energy flexibility of residential hot water systems using data from a large scale real world pilot. All the houses considered in this analysis feature identical air source heat pump hot water systems, along with 200 litre storage vessels. It is shown that ambient conditions, control algorithm and occupant behaviour, all influence the available energy flexibility of the hot water system, albeit in different ways. Available capacity for energy flexibility and the corresponding recovery periods can differ by as much as two to four times for identical storage, meaning that these differences should be taken into account during operational planning with flexible loads. Additionally, it is shown that the implemented control strategy provides a meaningful avenue to alter the available energy flexibility. The paper also highlights key differences in the way these factors influence the overall energy demand when compared to available flexibility.

Energy communities will play a central role in the sustainable energy transition by helping inform and engage end users to become more responsible consumers of energy. However, the true potential of energy communities can only be unlocked at scale. This scalability requires data-driven solutions that model not just the behaviour of building occupants but also of energy flexible resources in buildings, distributed generation and grid conditions in general. This understanding can then be utilized to improve the design and operation of energy communities in a variety of real-world settings. However, in practice, collecting and analysing the data necessary to realize these objectives forms a large part of such projects, and is often seen as a prohibitive stumbling block. Furthermore, without a proper understanding of the local context, these projects are often at risk of failure due to misplaced expectations. However, this process can be considerably accelerated by utilizing open source datasets and models from related projects, which have been carried out in the past. Likewise, a number of open source, general-purpose tools exist that can help practitioners design and operate LECs in a near-optimal manner. These resources are important because they not only help ground expectations, they also provide LECs and other relevant stakeholders, including utilities and distribution system operators, with much-needed visibility on future energy and cash flows. This review provides a detailed overview of these open-source datasets, models and tools, and the many ways they can be utilized in optimally designing and operating real-world energy communities. It also highlights some of the most important limitations in currently available open source resources, and points to future research directions. Highlights:1. The importance of open-source datasets and tools for local energy communities 2. Common use cases for open-source datasets, models and tools for energy communities 3. A thorough review of electricity demand and meteorological datasets and models 4. Most important shortcomings with currently available datasets, models and tools

A general approach is proposed to determine occupant behavior (occupancy and activity) in offices and residential buildings in order to use these estimates for improved energy management. Occupant behavior is modelled with a Bayesian network in an unsupervised manner. This algorithm makes use of domain knowledge gathered via questionnaires and recorded sensor data for motion detection, power, and hot water consumption as well as indoor CO2 concentration. Different case studies have been investigated with diversity according to their context (available sensors, occupancy or activity feedback, complexity of the environment, etc.). Furthermore, experiments integrating occupancy estimation and hot water production control show that energy efficiency can be increased by roughly 5% over known optimal control techniques and more than 25% over rule-based control while maintaining the same occupant comfort.