• Energy Research
• 2025-2025close
• 7. Clean energy close
• CN close
• CA close

Energy forecasting is an essential task in power system operations. Operators usually issue forecasts and leverage them to schedule energy dispatch ahead of time. However, forecast models are typically developed in a way that overlooks the operational value of the forecasts. To bridge the gap, we design a value-oriented point forecasting approach for sequential energy dispatch problems with renewable energy sources. At the training phase, we align the loss function with the overall operation cost function, thereby achieving reduced operation costs. The forecast model parameter estimation is formulated as a bilevel program. Under mild assumptions, we convert the upper-level objective into an equivalent form using the dual solutions obtained from the lower-level operation problems. Additionally, a novel iterative solution strategy is proposed for the newly formulated bilevel program. Under such an iterative scheme, we show that the upper-level objective is locally linear regarding the forecast model output, and can act as the loss function. Numerical experiments demonstrate that, compared to commonly used statistical quality-oriented point forecasting methods, forecasts obtained by the proposed approach result in lower operation costs. Meanwhile, the proposed approach is more computationally efficient than traditional two-stage stochastic programs. Accepted in IEEE Transactions on Smart Grid

Recent years have witnessed exponential growth in developing deep learning models for time series electricity forecasting in power systems. However, most of the proposed models are designed based on the designers’ inherent knowledge and experience without elaborating on the suitability of the proposed neural architectures. Moreover, these models cannot be self-adjusted to dynamically changed data patterns due to the inflexible design of their structures. Although several recent studies have considered the application of the neural architecture search (NAS) technique for obtaining a network with an optimized structure in the electricity forecasting sector, their training process is computationally expensive and their search strategies are not flexible, indicating that the NAS application in this area is still at an infancy stage. In this study, we propose an intelligent automated architecture search (IAAS) framework for the development of time series electricity forecasting models. The proposed framework contains three primary components, that is, network function–preserving transformation operation, reinforcement learning–based network transformation control, and heuristic network screening, which aim to improve the search quality of a network structure. After conducting comprehensive experiments on two publicly available electricity load data sets and two wind power data sets, we demonstrate that the proposed IAAS framework significantly outperforms the 10 existing models or methods in terms of forecasting accuracy and stability. Finally, we perform an ablation experiment to showcase the importance of critical components in the proposed IAAS framework in improving forecasting accuracy. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: J. Yang, G. Jiang, and Y. Chen were supported by the National Natural Science Foundation of China [Grants 72293562, 72121001, 72101066, 72131005, 71801148, and 72171060]. Y. Chen was supported by the Heilongjiang Natural Science Excellent Youth Fund [YQ2022G004]. Supplemental Material: The software ( Yang et al. 2023 ) that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0034 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0034 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Le carbone noir est une petite particule d'aérosol (ou aérienne) de courte durée de vie liée au réchauffement climatique et aux effets nocifs sur la santé. Il est rejeté par la combustion incomplète de carburants à base de carbone (c.-à-d. les combustibles fossiles, les biocarburants ou le bois) sous la forme de matière particulaire très fine. Le carbone noir n'est pas rejeté seul, mais en tant que composante d'une matière particulaire d'un diamètre inférieur ou égal à 2,5 micromètres (PM2,5). En tant que membre du Conseil de l'Arctique, le Canada est engagé à produire un inventaire annuel des émissions de carbone noir. Ces données serviront à informer les Canadiens au sujet des émissions de carbone noir et à fournir des renseignements inestimables pour l'élaboration de stratégies de gestion de la qualité de l'air. Les données utilisées pour la compilation du rapport proviennent des sections de l'Inventaire des émissions de polluants atmosphériques (IEPA) en particulier pour les émissions de matières particulaires fines (PM2,5) provenant de sources liées à la combustion Renseignements supplémentaires Pour un complément d'information sur l'Inventaire des émissions de carbone noir du Canada, consulter : https://Canada.ca/carbone-noir Pour les émissions canadiennes d'autres polluants atmosphériques, se reporter à l'Inventaire des émissions de polluants atmosphériques : https://www.canada.ca/fr/environnement-changement-climatique/services/polluants/inventaire-emissions-atmospheriques-apercu.html Outil d'interrogation interactif de l'IEPA et carbone noir : https://pollution-waste.canada.ca/air-emission-inventory/?GoCTemplateCulture=fr-CA Soutien aux projets : Inventaire des émissions de carbone noir au Canada 2013-2023 Black carbon is a short-lived, small aerosol (or airborne) particle linked to both climate warming and adverse health effects. It is emitted from incomplete combustion of carbon-based fuels (i.e., fossil fuels, biofuels, wood) in the form of very fine particulate matter. Black carbon is not emitted on its own, but as a component of particulate matter less than or equal to 2.5 micrometres in diameter (PM2.5). As a member of the Arctic Council, Canada has committed to producing an annual inventory of black carbon emissions. This data will serve to inform Canadians about black carbon emissions and provide valuable information for the development of air quality management strategies. The data used to compile the report originate from sections of the Air Pollutant Emission Inventory (APEI) specifically fine particulate matter (PM2.5) emissions from combustion-related sources. Supplemental Information For more information on Canada's Black Carbon Inventory, please visit: https://Canada.ca/black-carbon For Canada's emissions of other air pollutants, please reference the Air Pollutant Emission Inventory: https://Canada.ca/APEI APEI and Black Carbon Interactive Query Tool: https://pollution-waste.canada.ca/air-emission-inventory Supporting Projects: Canada's Black Carbon Inventory for 2013-2023

Successful applicants of the 2017-2019 Solar for Community Buildings Program, that enables eligible community groups and organizations to generate solar photovoltaic (PV) electricity on their roofs or properties

Global climate challenge is demanding urgent actions for decarbonization, while electric power systems take the major roles in clean energy transition. Due to the existence of spatially and temporally dispersed renewable energy resources and the uneven distribution of carbon emission intensity throughout the grid, it is worth investigating future load planning and demand management to offset those generations with higher carbon emission rates. Such techniques include inter-region utilization of geographically shiftable resources and stochastic renewable energy. For instance, data center is considered to be a major carbon emission producer in the future due to increasing information load, while it holds the capability of geographical load balancing. In this paper, we propose a novel planning and operation model minimizing the system-level carbon emissions via sitting and operating geographically shiftable resources. This model decides the optimal locations for shiftable resources expansion along with power dispatch schedule. To accommodate future system operation patterns and a wide range of operating conditions, we incorporate 20-year fine-grained load and renewables scenarios for grid simulations of realistic sizes (e.g., up to 1888 buses). To tackle the computational challenges coming from the combinatorial nature of such large-scale planning problem, we develop a customized Monte Carlo Tree Search (MCTS) method, which can find reasonable solutions satisfying solution time limits. Besides, MCTS enables flexible time window settings and offline solution adjustments. Extensive simulations validate that our planning model can reduce more than 10\% carbon emission across all setups. Compared to off-the-shelf optimization solvers such as Gurobi, our method achieves up to 8.1X acceleration while the solution gaps are less than 1.5\% in large-scale cases. Accepted at IEEE Transactions on Power Systems

In order to solve the problem of excessive burden of electricity and energy consumption in urban landscape buildings clusters, the study combined data mining algorithms to establish a prediction model for energy-saving renovation of urban landscape building clusters. Firstly, the energy demand and energy consumption of theurban landscape buildings complex were analysed, a mathematical model was established to predict the energy consumption of the building complex. Then, the prediction model of energy-saving retrofitting of building clusters was constructed by combining data mining techniques. The experimental results show that the change trend of total energy consumption is different under different single influencing factors of energy consumption. Among them, the lighting power density factor has the greatest influence on energy consumption, and its annual energy consumption change rate can reach about 0.35. Applying the prediction model to the energy consumption prediction of 15 urban single buildings, it was found that the total energy consumption of the buildings before the retrofit was much higher than that after the retrofit, and the energy-saving rate of the whole observed sample building group was as high as 18.5%, meanwhile, the highest energy-saving rate of the single buildings reached 30.1%.

Le programme des Indicateurs canadiens de durabilité de l'environnement (ICDE) rend compte de la performance du Canada à l'égard d'enjeux clés en matière de développement durable. L'indicateur sur les Émissions de gaz à effet de serre des installations d'envergure présente les émissions totales de gaz à effet de serre provenant des plus grands émetteurs de gaz à effet de serre au Canada pour l'année de déclaration 2023. Le Programme de déclaration de gaz à effet de serre assure le suivi et la déclaration obligatoire des émissions de gaz à effet de serre par les plus grands émetteurs du Canada. Cette déclaration obligatoire contribue au développement, à la mise en œuvre et à l'évaluation des politiques et stratégies du Canada en matière de changements climatiques et d'énergie. Les données sur les émissions de gaz à effet de serre déclarées dans le cadre du Programme de déclaration des gaz à effet de serre sont utilisées pour le développement des estimations des émissions de gaz à effet de serre au Canada dans le Rapport d'inventaire national et pour appuyer les initiatives de règlementation. Cette information est rendue disponible aux Canadiens sous plusieurs formats : cartes statiques et interactives, figures et graphiques, tableaux de données HTML et CSV et rapports téléchargeables. Voir la documentation supplémentaire pour les sources des données et pour lire comment les données sont collectées et comment l'indicateur est calculé. Indicateurs canadiens de durabilité de l'environnement : https://www.canada.ca/indicateurs-environnementaux The Canadian Environmental Sustainability Indicators (CESI) program provides data and information to track Canada's performance on key environmental sustainability issues. The Greenhouse gas emissions from large facilities indicator reports total greenhouse gas emissions from the largest greenhouse gas emitters in Canada for the 2023 reporting year. The Greenhouse Gas Reporting Program ensures that the greenhouse gas emissions from Canada's largest emitters are tracked and reported. This mandatory reporting contributes to the development, implementation and evaluation of climate change and energy policies and strategies in Canada. Greenhouse gas emissions data reported through the Greenhouse Gas Reporting Program are used to inform the development of estimates of greenhouse gas emissions in Canada in the National Inventory Report, and to support regulatory initiatives. Information is provided to Canadians in a number of formats including: static and interactive maps, charts and graphs, HTML and CSV data tables and downloadable reports. See the supplementary documentation for the data sources and details on how the data were collected and how the indicator was calculated. Canadian Environmental Sustainability Indicators: https://www.canada.ca/environmental-indicators

Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch. The integration of renewable energy sources and the occurrence of external events, such as the COVID-19 pandemic, have rapidly increased uncertainties in load forecasting. The uncertainties in load forecasting can be divided into two types: epistemic uncertainty and aleatoric uncertainty. Separating these types of uncertainties can help decision-makers better understand where and to what extent the uncertainty is, thereby enhancing their confidence in the following decision-making. This paper proposes a diffusion-based Seq2Seq structure to estimate epistemic uncertainty and employs the robust additive Cauchy distribution to estimate aleatoric uncertainty. Our method not only ensures the accuracy of load forecasting but also demonstrates the ability to separate the two types of uncertainties and be applicable to different levels of loads. The relevant code can be found at \url{https://anonymous.4open.science/r/DiffLoad-4714/}. Accepted by IEEE Transactions on Power Systems, 2024

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.