Short-Term Energy Forecasting Using Machine-Learning-Based Ensemble Voting Regression

Meeting the required amount of energy between supply and demand is indispensable for energy manufacturers. Accordingly, electric industries have paid attention to short-term energy forecasting to assist their management system. This paper firstly compares multiple machine learning (ML) regressors during the training process. Five best ML algorithms, such as extra trees regressor (ETR), random forest regressor (RFR), light gradient boosting machine (LGBM), gradient boosting regressor (GBR), and K neighbors regressor (KNN) are trained to build our proposed voting regressor (VR) model. Final predictions are performed using the proposed ensemble VR and compared with five selected ML benchmark models. Statistical autoregressive moving average (ARIMA) is also compared with the proposed model to reveal results. For the experiments, usage energy and weather data are gathered from four regions of Jeju Island. Error measurements, including mean absolute percentage error (MAPE), mean absolute error (MAE), and mean squared error (MSE) are computed to evaluate the forecasting performance. Our proposed model outperforms six baseline models in terms of the result comparison, giving a minimum MAPE of 0.845% on the whole test set. This improved performance shows that our approach is promising for symmetrical forecasting using time series energy data in the power system sector.

Accumulation of Glycogen and Upregulation of LEA-1 in C. elegans daf-2(e1370) Support Stress Resistance, Not Longevity

DAF-16-dependent activation of a dauer-associated genetic program in the C. elegans insulin/IGF-1 daf-2(e1370) mutant leads to accumulation of large amounts of glycogen with concomitant upregulation of glycogen synthase, GSY-1. Glycogen is a major storage sugar in C. elegans that can be used as a short-term energy source for survival, and possibly as a reservoir for synthesis of a chemical chaperone trehalose. Its role in mitigating anoxia, osmotic and oxidative stress has been demonstrated previously. Furthermore, daf-2 mutants show increased abundance of the group 3 late embryogenesis abundant protein LEA-1, which has been found to act in synergy with trehalose to exert its protective role against desiccation and heat stress in vitro, and to be essential for desiccation tolerance in C. elegans dauer larvae. Here we demonstrate that accumulated glycogen is not required for daf-2 longevity, but specifically protects against hyperosmotic stress, and serves as an important energy source during starvation. Similarly, lea-1 does not act to support daf-2 longevity. Instead, it contributes to increased resistance of daf-2 mutants to heat, osmotic, and UV stress. In summary, our experimental results suggest that longevity and stress resistance can be uncoupled in IIS longevity mutants.

Techno-economic analysis of hydrogen production from PV plants

Hydrogen production through electrolysis from renewable sources is expected to play an important role to achieve the reduction targets of carbon dioxide emissions set for the next decades. Electrolysers can use the renewable energy surplus to produce green hydrogen and contribute to making the electrical grid more stable. Hydrogen can be used as medium-long term energy storage, converted into other fuels, or used as feedstock in industry thus contributing to decarbonise hard-to-abate-sectors. However, due to the intermittent and variable nature of solar and wind power, the direct coupling of electrolysers with renewables may lead to high production fluctuations and frequent shutdowns. As a consequence, accelerated electrolyser degradation and safety issues related to low load operation may arise. In this study, simulations of hydrogen production with an electrolyser fed by a PV system are performed in Matlab for a reference year. The effect of PV power fluctuations on the electrolyser operation and production is investigated. The impact of the electrolyser size for a fixed nominal power of the PV plant is also analysed from both energetic and economic points of view.

Energy Resilience Impact of Supply Chain Network Disruption to Military Microgrids

The ability to provide uninterrupted power to military installations is paramount in executing a country’s national defense strategy. Microgrid architectures increase installation energy resilience through redundant local generation sources and the capability for grid independence. However, deliberate attacks from near-peer competitors can disrupt the associated supply chain network, thereby affecting mission critical loads. Utilizing an integrated discrete-time Markov chain and dynamic Bayesian network approach, we investigate disruption propagation throughout a supply chain network and quantify its mission impact on an islanded microgrid. We propose a novel methodology and an associated metric we term “energy resilience impact” to identify and address supply chain disruption risks to energy security. The proposed methodology addresses a gap in the literature and practice where it is assumed supply chains will not be disrupted during incidents involving microgrids. A case study of a fictional military installation is presented to demonstrate how installation energy managers can adopt this methodology for the design and improvement of military microgrids. The fictional case study shows how supply chain disruptions can impact the ability of a microgrid to successfully supply electricity to critical loads throughout an islanding event.

Germany’s Position Concerning the Nord Stream-2 Pipeline in the Context of the European Union’s Energy Security

The article analyzes the underlying foundations of Germany’s position regarding the construction of Russia’s Nord Stream 2 pipeline, as well as the impact of this position on the European Union’s joint energy policy and energy security. Against the backdrop of the constantly growing energy consumption both globally and in EU specifically, supplies of energy sources are being increasingly used by exporters as an instrument of political influence. In this context, the Nord Stream-2 pipeline is regarded as such an instrument, primarily by Russia itself. On the other hand, Germany has been supporting the construction of the new Russian pipeline due to the need to ensure uninterrupted supply of cheap natural gas. The latter is of particular significance for the realization of Germany’s long-term energy transformation strategy. However, by sticking to such a position, Germany prioritizes its own political and economic interests over those of EU and individual Member States, which is contrary to one of the main principles of EU’s functioning, i.e., the principle of solidarity. With this in mind, it is reasonable to conclude that Germany is almost single-handedly defining the strategic direction of the entire EU’s energy policy without paying due attention to alternative suppliers and sources.

A Long-Term Energy-Rich Diet Increases Prefrontal BDNF in Sprague-Dawley Rats

Findings of the effect of high-fat feeding including “Cafeteria Diets” (CAF) on brain-derived neurotrophic factor (BDNF) in the hippocampus (HIP) and prefrontal cortex (PFC) in rodents are conflicting. CAF is a non-standardized, highly palatable energy-rich diet composed by everyday food items for human consumption and is known to induce metabolic syndrome and obesity in rats. However, the highly palatable nature of CAF may counteract a negative effect of chronic stress on anticipatory behavior and synaptic plasticity in the hippocampus, hence represent a confounding factor (e.g., when evaluating functional effects on the brain). This study investigated the effects of a chronic, restricted access to CAF on BDNF, monoamine neurotransmitters, and redox imbalance in HIP and PFC in male rats. Our results show that CAF induced BDNF and its receptor TrkB in PFC compared to the controls (p < 0.0005). No differences in monoamine neurotransmitters were detected in either PFC or HIP. CAF increased dehydroascorbic acid and decreased malondialdehyde in PFC (p < 0.05), suggesting an early redox imbalance insufficient to induce lipid peroxidation. This study supports that a chronic CAF on a restricted schedule increases BDNF levels in the PFC of rats, highlighting that this may be a suboptimal feeding regime when investigating the effects of diet-induced obesity in the brain and emphasizing this as a point of attention when comparing the findings.

Points of Consideration on Climate Adaptation of Solar Power Plants in Thailand: How Climate Change Affects Site Selection, Construction and Operation

Solar energy is planned to undergo large-scale deployment along with Thailand’s transformation to a carbon neutral society in 2050. In the course of energy transformation planning, the issue of energy infrastructure adaptation to climate change has often been left out. This study aims to identify climate-related risks and countermeasures taken in solar power plants in Thailand using thematic analysis with self-administered observations and structured interviews in order to propose points of consideration during long-term energy planning to ensure climate adaptation capacity. The analysis pointed out that floods and storms were perceived as major climate events affecting solar power plants in Thailand, followed by lightning and fires. Several countermeasures were taken, including hard countermeasures that require extensive investment. Following policy recommendations were derived from the climate-proofing investment scenario study. Policy support in terms of enabling regulations or financial incentives is needed for implementation of climate-proofing countermeasures. Public and private sectors need to secure sufficient budget for fast recovery after severe climate incidents. Measures must be taken to facilitate selection of climate-resilient sites by improving conditions of power purchase agreement or assisting winning bidders in enhancing climate adaptability of their sites. These issues should be considered during Thailand’s long-term energy planning.

A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction

Using extensive databases and known algorithms to predict short-term energy consumption comprises most computational solutions based on artificial intelligence today. State-of-the-art approaches validate their prediction models in offline environments that disregard automation, quality monitoring, and retraining challenges present in online scenarios. The existing demand response initiatives lack personalization, thus not engaging consumers. Obtaining specific and valuable recommendations is difficult for most digital platforms due to their solution pattern: extensive database, specialized algorithms, and using profiles with similar aspects. The challenges and present personalization tactics have been researched by adopting a digital twin model. This study creates a different approach by adding structural topology to build a new category of recommendation platform using the digital twin model with real-time data collected by IoT sensors to improve machine learning methods. A residential study case with 31 IoT smart meter and smart plug devices with 19-month data (measurements performed each second) validated Digital Twin MLOps architecture for personalized demand response suggestions based on online short-term energy consumption prediction.