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.

No abstract

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

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.

Information systems (IS) projects represent key building blocks of large-scale digital transformation initiatives. As a result, IS projects have become increasingly ambitious in terms of both goals and scale, making it even more challenging for managers to exercise control over such projects. While prior research primarily focused on the direct and interactive effects of formal and informal control modes on IS project performance, recent research directs attention to the importance of considering project managers' control styles (i.e., how managers interact with controlees to enact controls). Corresponding studies also indicate that 'either/or' control approaches-as opposed to 'both/and' approaches-seem no longer viable in today's complex environment. Drawing on a control ambidexterity perspective, the study at hand theoretically develops and empirically tests the direct and interactive effects of controlstyle ambidexterity on IS project performance. Using matched-pair data from 146 IS projects (from 146 high-tech firms), we find that control-style ambidexterity improves project performance-directly and in combination with both formal and informal control. Our study contributes to developing a more comprehensive understanding of effective IS project control tactics, which can help explain mixed findings in prior literature and thus support continued theory development in the research area.