Welcome to this edition of Energy (Proceedings of the ICE). The subject of energy was once of limited interest, mostly to engineers and some academics, but today it is everywhere from the news media to the many social media platforms where people argue with more or less expertise about subjects like net-zero, renewable energy and the pros and cons of heat pumps. It is possible to make a living fronting YouTube channels that present energy subjects to interested sections of the global public. Good energy researchers will find a large audience eager to hear how mankind will tackle the related challenges of climate change, energy cost and energy security. As a retired academic I spend some of my time advising churches in my region as they consider how best to respond Church of England’s aim to reach net-zero and to be ecologically responsible. Of course many of our church buildings produced almost no emissions for hundreds of years but they were often very uncomfortable places as a result. Today we try to use technologies such as solar PV and heat pumps to provide comfort while trying to minimise the cost of heating and its impact on the appearance of these beautiful buildings.
The challenge of balancing the often competing requirements for security, low cost and sustainability is sometimes known as a trilemma and engineers often use mathematical modelling when trying to optimise their designs against these objectives. The theme of this issue is mathematical modelling of energy systems and it features four different types of energy systems whose designs are optimised using different mathematical methods.
As the birthplace of the industrial revolution, the UK can be regarded as the world’s first ‘high carbon economy’ so it is encouraging to see UK researchers developing new techniques to reduce emissions from the most important energy consuming sector of the economy, which is buildings. Khalil et al. (2025) describe the application of data driven methods such as machine learning and deep learning to predict the performance of buildings into the future when the climate in which they operate is also changing. In the case of the UK, building emissions are forecast to be related more to cooling load and less to heating load, and their paper shows how a data driven approach can help both to reduce the cost of modelling building performance and to mitigate the effects of uncertainty around future climate trends when trying to design buildings that will perform well into the future.
The subject of clean energy is no less important in developing countries, and here engineers have an advantage of being able to apply renewable energy technologies with less concern for the impact on legacy assets. One of the key issues relevant to developed and developing countries is where best to locate energy assets to maximise the return on investment. Zenebe et al. (2025) present a study into the optimal location of wind farms and the selection of suitable turbine designs with respect to income generation and meeting the needs of local people in Ethiopia. The methods used are reliable and well understood but their application in an African context is instructive.
One of the known drawbacks of wind power is the variability of generation, which often does not match the variability of demand for power. This may matter less to the people of rural Ethiopia where any electrical power is a welcome alternative to expensive and polluting fossil fuels, but in more developed regions the variability of renewable energy can often be a major obstacle to wider adoption of clean technologies.
This presents a serious challenge to wider adoption of solar thermal technologies since the energy delivered is often both highly variable and most available when it is least needed. One solution to this is the use of seasonal thermal storage, but since such systems can be very expensive to install their design is best carried out with careful modelling of dynamic performance. Xu at el. (2025) present a two year study of a heating system for a large commercial building in which a seasonal thermal store in the form of an underground water tank is used to support heating. In the winter the output from solar thermal collectors is much lower than in the summer when the solar collectors replenish the thermal store, however as with many such systems, the stored summer heat is insufficient to meet the entire winter load, so the system augments with an electrical heater in winter. The cost of this is included in the model in order to optimise the overall system design. Because the additional heat source is electric, the system is capable of heating the building without producing any emissions at all, depending on the carbon content of grid electricity. This depends on the time of use, which is also modelled since the price varies in time.
Another high emission sector of most developed economies is the transport sector. This is often very difficult to decarbonise due to a high penetration and high dependency on fossil fuels. A popular way to partly mitigate the emissions from internal combustion engines is to blend their fossil-based fuels with less polluting fuels such as ethanol. The last paper in this issue (Dey et al., 2025) describes a study that aims to predict the likely demand for gasoline in India up to 2030 and the impact this will have on demand for ethanol as the nation moves towards 100% deployment of E20. The paper compares different statistical forecasting tools before choosing the long short-term memory (LSTM) method to forecast Indian demand for ethanol to 2030. The results of this study are important for Indian policy makers if they are to stimulate the local production of ethanol in their economy to avoid continuing shortages of this fuel.
I hope you enjoy the variety of the papers in this issue. Please remember you can access more recent papers in the Ahead of Print section on the ICE Virtual Library at www.icevirtuallibrary.com/toc/jcien/0/0.
