Within the context of the Smart City, the need for intelligent approaches to manage and coordinate the diverse range of supply and conversion technologies and demand applications has been well established. The wide-scale proliferation of sensors coupled with the implementation of embedded computational intelligence algorithms can help to tackle many of the technical challenges associated with this energy systems integration problem. Nonetheless, barriers still exist, as suitable methods are needed to handle complex networks of actors, often with competing objectives, while determining design and operational decisions for systems across a wide spectrum of features and time-scales. This review looks at the current developments in the smart energy sector, focussing on techniques in the main application areas along with relevant implemented examples, while highlighting some of the key challenges currently faced and outlining future pathways for the sector. A detailed overview of a framework developed for the EU H2020 funded Sharing Cities project is also provided to illustrate the nature of the design stages encountered and control hierarchies required. The study aims to summarise the current state of computational intelligence in the field of smart energy management, providing insight into the ways in which current barriers can be overcome.

As Internet of Things (IoT) technologies enable greater communication between energy assets in smart cities, the operational coordination of various energy networks in a city or district becomes more viable. Suitable tools are needed that can harness advanced control and machine learning techniques to achieve environmental, economic and resilience objectives. In this paper, an energy management tool is presented that can offer optimal control, scheduling, forecasting and coordination services to energy assets across a district, enabling optimal decisions under user-defined objectives. The tool presented here can coordinate different subsystems in a district to avoid the violation of high-level system constraints and is designed in a generic fashion to enable transferable use across different energy sectors. The work demonstrates the potential for a single open-source optimisation framework to be applied across multiple energy vectors, providing local government the opportunity to manage different assets in a coordinated fashion. This is shown through case studies that integrate low-carbon communal heating for social housing with electric vehicle charge-point management to achieve high-level system constraints and local government objectives in the borough of Greenwich, London. The paper illustrates the theoretical methodology, the software architecture and the digital twin-based testing environment underpinning the proposed approach.

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Food and garden waste are important components of organic fraction municipal solid waste (OFMSW), representing carbon and nutrient rich resources composed of carbohydrates, lipid, protein, cellulose, hemicellulose, and lignin. Despite progressive diversion from landfill, over 50% of landfilled MSW is biodegradable, causing greenhouse gas emissions. In conventional waste management value chains, OFMSW components have been regarded as byproducts as opposed to promising resources with energy and nutrient values. Full exploitation of waste resources calls for a value chain transformation toward proactive resource recovery and waste commoditization. This requires robust projection of OFMSW composition and supply variability. Gradient boosting models are developed here using historical socio-demographic, weather, and waste data from U.K. local authorities. These models are used to forecast garden and food OFMSW generation for each of the 327 U.K. local authorities. The developed methods perform particularly well in forecasting garden waste due to a greater link to measurable environmental variables. The research highlights the key influences in waste volume prediction and demonstrates the difficulty in transferring models to local authorities without training data. The predictive performance and spatial granularity of model projections offer a promising approach to inform decision-making on future waste recovery facilities and OFMSW commoditization.

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