randomLCA: An R Package for Latent Class with Random Effects Analysis

Latent class is a method for classifying subjects, originally based on binary outcome data but now extended to other data types. A major difficulty with the use of latent class models is the presence of heterogeneity of the outcome probabilities within the true classes, which violates the assumption of conditional independence, and will require a large number of classes to model the association in the data resulting in difficulties in interpretation. A solution is to include a normally distributed subject level random effect in the model so that the outcomes are now conditionally independent given both the class and random effect. A further extension is to incorporate an additional period level random effect when subjects are observed over time. The use of the randomLCA R package is demonstrated on three latent class examples: classification of subjects based on myocardial infarction symptoms, a diagnostic testing approach to comparing dentists in the diagnosis of dental caries and classification of infants based on respiratory and allergy symptoms over time

CLOVER: A modelling framework for sustainable community-scale energy systems

Sustainable Development Goal 7 aims to provide sustainable, affordable, reliable and modern energy access to all by 2030 (United Nations, 2015). In order for this goal to be achieved, sustainable energy interventions in developing countries must be supported with design tools which can evaluate the technical performance of energy systems as well as their economic and climate impacts. CLOVER (Continuous Lifetime Optimisation of Variable Electricity Resources) is a software tool for simulating and optimising community-scale energy systems, typically minigrids, to support energy access in developing countries (Winchester et al., 2022). CLOVER can be used to model electricity demand and supply at an hourly resolution, for example allowing users to investigate how an electricity system might perform at a given location. CLOVER can also identify an optimally-sized energy system to meet the needs of the community under specified constraints. For example, a user could define an optimum system as one which provides a desired level of reliability at the lowest cost of electricity. CLOVER can provide an insight into the technical performance, costs, and climate change impact of a system, and allow the user to evaluate many different scenarios to decide on the best way to provide sustainable, affordable and reliable electricity to a community. CLOVER can be used on both personal computers and high-performance computing facilities. Its design separates its general framework (code, contained in a source src directory) from user inputs (data, contained in a directory entitled locations) which are specific to their investigations. The user inputs these data via a combination of .csv and .yaml files. CLOVER’s straightforward command-line interface provides simple operation for both experienced Python users and those with little prior exposure to coding. An installable package, clover-energy, is available for users to download without needing to become familiar with GitHub’s interface. Information about CLOVER and how to use it is available on the CLOVER wiki pages

Expanding the Frontiers of Information Systems Research: Introduction to the Special Issue

An introduction to the Expanding the Frontiers of Information Systems Research special issue

Maximising the benefits of renewable energy infrastructure in displacement settings: optimising the operation of a solar-hybrid mini-grid for institutional and business users in Mahama Refugee Camp, Rwanda

Humanitarian organisations typically rely on expensive, polluting diesel generators to provide power for services in refugee camps, whilst camp residents often have no access to electricity. Integrating solar and battery storage capacity into existing diesel-based systems can provide significant cost and emissions savings and offer an opportunity to provide power to displaced communities. By analysing monitored demand data and using computational energy system modelling, we assess the savings made possible by the integration of solar (18.4 kWp) and battery (78 kWh) capacity into the existing diesel-powered mini-grid in Mahama Refugee Camp, Rwanda. We find that the renewables infrastructure reduces fuel expenditure by $41,500 and emissions by 44 tCO2eq (both 74$4100 and 12.4 tCO2eq, using 33% of battery lifetime versus 15% under the original strategy. This reduces the cost of electricity by 33% versus diesel generation alone, whilst more aggressive cycling strategies could prove economical if moderate battery price decreases are realised. Extending the system to businesses in the camp marketplace can completely offset the system fuel costs if the mini-grid company charges customers the same tariff as the one it uses in the host community, but not the national grid tariff. Humanitarian organisations and the private sector should explore opportunities to integrate renewables into existing diesel-based infrastructure, and optimise its performance once installed, to reduce costs and emissions and provide meaningful livelihood opportunities to displaced communities

The cost and emissions advantages of incorporating anchor loads into solar mini-grids in India

Renewables-based mini-grids have the potential to improve electricity access with lower emissions and better reliability than national grids. However, these systems have a challenging cost to revenue ratio, hindering their implementation. Combining residential loads with an anchor load, a relatively large non-domestic user, can help to improve mini-grid economics. Using measured electricity demand data from India and energy modelling, we assess the cost and emissions advantages of integrating health clinics as anchor loads within domestic solar mini-grids. For comparison, we also assess the ability of the national grid to meet our demand scenarios using monitored grid data. We apply a scenario-based approach, using separate domestic and anchor load demand profiles, and both in combination; we test meeting two levels of energy demand, 95% and 100%; and compare systems using PV and batteries, diesel, and hybrid generation. We find that the national grid has poor availability, at just over 50% at the most comparable monitoring site; and that it would meet a lower fraction of energy demand for our anchor load scenarios than the domestic only ones. For the off-grid systems, we find substantial cost and emissions reductions with anchor loads relative to demand scenarios without anchor loads. At 95% of demand met, we find PV and battery systems are 14-22% cheaper than diesel-only systems, with 10 times lower carbon intensity. Our findings illustrate the role off-grid systems can play in the provision of reliable low-carbon electricity and highlight the advantages of incorporating anchor loads like health centres into such systems

SITAR—a useful instrument for growth curve analysis

Background Growth curve analysis is a statistical issue in life course epidemiology. Height in puberty involves a growth spurt, the timing and intensity of which varies between individuals. Such data can be summarized with individual Preece–Baines (PB) curves, and their five parameters then related to earlier exposures or later outcomes. But it involves fitting many curves