The energy sector is experiencing rapid transformation due to increasing renewable energy integration, decentralisation of power systems, and a heightened focus on efficiency and sustainability. With energy demand becoming increasingly dynamic and generation sources more variable, advanced forecasting and optimisation strategies are crucial for maintaining grid stability, cost-effectiveness, and environmental sustainability. This paper explores emerging paradigms in energy forecasting and management, emphasizing four critical domains: Energy Demand Forecasting integrated with Weather Data, Building Energy Optimisation, Heat Network Optimisation, and Energy Management System (EMS) Optimisation within a System of Systems (SoS) framework. Leveraging machine learning techniques and Model Predictive Control (MPC), the study demonstrates substantial enhancements in energy efficiency across scales -- from individual buildings to complex interconnected energy networks. Weather-informed demand forecasting significantly improves grid resilience and resource allocation strategies. Smart building optimisation integrates predictive analytics to substantially reduce energy consumption without compromising occupant comfort. Optimising CHP-based heat networks achieves cost and carbon savings while adhering to operational and asset constraints. At the systems level, sophisticated EMS optimisation ensures coordinated control of distributed resources, storage solutions, and demand-side flexibility. Through real-world case studies we highlight the potential of AI-driven automation and integrated control solutions in facilitating a resilient, efficient, and sustainable energy future.
Aquaculture plays a critical role in global food and nutrition security, supporting livelihoods and providing high-quality protein to nearly three billion people. However, rapid sectoral intensification has raised significant environmental concerns, particularly regarding greenhouse gas (GHG) emissions associated with feed production, energy consumption, and biogeochemical processes in aquatic systems. This review provides a comprehensive and structured synthesis of current knowledge on aquaculture carbon footprints and advances the field by proposing an integrated Net-Zero Aquaculture (NZA) framework that links carbon accounting, technological innovation, and policy mechanisms for climate-resilient food production. Drawing on life-cycle assessment (LCA) studies, digital monitoring technologies, and nature-based solutions, the review identifies the key emission hotspots, including feed manufacturing, on-farm energy use, and methane (CH4) and nitrous oxide (N2O) emissions from sediments. It critically evaluates mitigation strategies such as feed optimization, renewable-energy integration, waste valorization, and ecosystem-based approaches, highlighting system-specific trade-offs and implementation challenges. The study further examines emerging Monitoring, Reporting, and Verification (MRV) methodologies tailored to aquaculture, emphasizing the need for standardized GHG accounting frameworks and enhanced digital traceability. In addition, the review assesses the role of policy and economic instruments, including carbon pricing, sustainability certification, and Environmental, Social, and Governance (ESG) investment frameworks, in accelerating decarbonization pathways. By integrating technological, ecological, and governance dimensions, this study presents a phased roadmap toward achieving carbon neutrality by 2050. Overall, it positions aquaculture as a pivotal component of sustainable blue-food systems while highlighting critical research gaps and priorities for scalable and equitable Net-Zero transitions.
For migratory species, successful navigation is critical to fitness. In Atlantic salmon, for example, there is evidence that during migration from natal streams to the sea, passage through waters with poorly defined or mixed water velocity patterns may constrain directional navigation, causing individuals to become trapped or delayed in lakes or other bodies with slowly flowing water. In this study, we determined the minimum water velocities needed to elicit a behavioural response, in this case a change in the direction of holding position, in both wild origin and domesticated salmon smolts. Smolts required a directional flow in excess of 8.9 cm s-1 to exhibit effective directional orientation towards the current. Smolts of a domesticated farm origin exhibited a similar qualitative and quantitative response as wild fish. These results suggest that, in areas where the downstream migrating Atlantic salmon smolts pass through low-directional water flow, it may be possible to manipulate directional flows above this minimum threshold, at least temporarily, as a management tool to increase migration success. This is likely to be particularly true where smolts are passing through dams, reservoirs or other impounded waters.
Migration is a high-risk behavior. For the Atlantic salmon, Salmo salar, migrating from its river nursery area to marine feeding grounds, the magnitude of risk varies with habitat type. Passage through lakes, in particular, is associated with low rates of migration success. Downstream migrating salmon smolts are rheotactic when migrating in rivers, but lakes typically provide poorer directional currents for migrating salmon. In this study we tested if, in the absence of clear navigational cues in lakes, Atlantic salmon smolts switch to a random search strategy to find the outflowing river. We constructed random search simulations to test if lake basin shape has an effect on migration success. We also compared simulated migration characteristics with migrations of salmon smolts through five real lakes for which there are telemetry data for migrating salmon. Correlated random walk simulations showed that a random search strategy could be successful for all lake shapes tested but was more successful in curved (round and elliptical) than rectangular basin shapes. Rectangular basins with the migration start and stop points at the ends of the lake had a higher success than those where these points were perpendicular to the axis of the lake. In general, a random walk model predicted the migration success rate of fish tracked through real lakes. However, for two lakes the simulated migration success exceeded that of actual success, suggesting that fish passing through these lakes were not adopting a random search strategy. We speculate that this is the result of either conflicting navigational cues which inhibit smolts from finding the lake outlet or that they temporarily suspend migration (e.g., to feed). Modelling predicted that for small lakes, directional swimming in short steps (ca. 100 m) followed by turns with very low variation from the direction of travel resulted in the highest migration success. For larger lakes, longer step lengths but also with low turn variation (simulated turning angle drawn from distributions of standard deviation 2° and 5° around a mean of 0°) resulted in the highest migration success. We conclude that navigation in downstream migrating salmon smolts switches from rheotaxis in rivers to a random search tactic in lakes except where residual flow cues in some lakes prevent this, at times resulting in suboptimal navigation outcomes.
Alterations to water management practices, in response to a growing demand to maximize energy production from renewable sources, threaten to exacerbate anticipated future water shortages caused by severe drought episodes, brought on by climate change. Across Scotland, many highly managed systems are inhabited by some of the last remaining reproductively viable populations of the freshwater pearl mussel, Margaritifera margaritifera. A lack of empirical evidence concerning mussel responses to alterations in flow is inhibiting the development of effective conservation management practices to prolonged drought. This study addressed this knowledge gap by examining the response of M. margaritifera to controlled water level drawdowns in laboratory and field settings. Using a laboratory flume, the responses of M. margaritifera from two different populations (a regulated system versus an unregulated system) were compared across rates of drawdown and density treatments. An analogous field trial was undertaken to examine the responses of M. margaritifera in a regulated system, with a hydroelectric dam facilitating a controlled drawdown. The study showed that M. margaritifera can detect alterations in flow depth, which culminate in the emersion of mussel beds, and respond by undertaking vertical and horizontal movements to mitigate the risk of prolonged aerial exposure. Results from the field trial corroborated findings from the flume experiments, with 80% of mussels shown to avoid emersion successfully by tracking receding water levels. Findings from this study support the role of controlled drawdowns in regulated rivers to reduce mortalities associated with receding water levels during prolonged low-flow episodes. Differences between populations in response highlight a need to adopt a context-dependent approach to conservation efforts.
