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# Grid Foresight

Tomorrow's Grid Today

## Sitemaps

- [XML Sitemap](https://gridforesight.ca/sitemap.xml): Contains all public & indexable URLs for this website.

## Posts

- [Planning the Distribution Grid of Tomorrow: Integrating Non-Wires Alternatives into Distribution Planning ](https://gridforesight.ca/planning-the-distribution-grid-of-tomorrow-integrating-non-wires-alternatives-into-distribution-planning/) - Distribution planning has traditionally relied on snapshot studies, where planners model feeder peak demand under normal conditions (N – 0) and contingency conditions (N – 1) to identify thermal overloads and voltage violations. Based on these assessments, utilities typically pursue conventional infrastructure upgrades such as reconductoring, load transfers, feeder additions, or substation expansion [1]. This approach was
- [Overcoming the Cold-Start Problem in EV Charging Forecasting](https://gridforesight.ca/overcoming-the-cold-start-problem-in-ev-charging-forecasting-2/) - Reliable smart charging requires forecasting the charging behavior of EVs. Deep learning algorithms could present a solution. However, deep neural networks (DNNs) require a large number of samples for training. Although there are available datasets for EV charging, such data is not available for newly committed EVs with limited historical charging records. Therefore, forecasting the
- [Overcoming the Cold-Start Problem in EV Charging Forecasting](https://gridforesight.ca/overcoming-the-cold-start-problem-in-ev-charging-forecasting/) - Overcoming the Cold-Start Problem in EV Charging Forecasting The rapid growth of electric vehicle (EV) adoption has been imposing pressure on distribution systems [1]. Voltage drop and congestion of feeders are the most common issues at the distribution networks level resulting from EV chargers. Smart charging is a well-accepted strategy to address these challenges [2].
- [AI Data Centers and the Grid: Where the Research and Practice Gaps Still Are](https://gridforesight.ca/ai-data-centers-and-the-grid-where-the-research-and-practice-gaps-still-are/) - AI Data Centers and the Grid: Where the Research and Practice Gaps Still Are
- [Grid-Ready, Revenue-Limited: The Regulatory Paradox of Transmission-Owned Storage](https://gridforesight.ca/grid-ready-revenue-limited-the-regulatory-paradox-of-transmission-owned-storage/) - Energy storage systems are increasingly recognized as uniquely flexible grid assets. Technically, they can inject and absorb power rapidly, respond to contingencies, support voltage, reduce overload risk, and help defer or complement conventional wires investments. That broad capability is part of why FERC Order No. 841 [1] required RTOs and ISOs to establish participation models
- [Making Transactive Energy Markets Carbon-Aware ](https://gridforesight.ca/making-transactive-energy-markets-carbon-aware/) - The Rise of Distributed Energy Resources Electric power systems (EDS) are changing rapidly. The growth of distributed energy resources (DERs), such as rooftop solar panels, battery storage, and small-scale generators, is transforming the traditional EDS. Consumers are no longer passive users but are becoming prosumers who both consume and produce energy. While this shift introduces new flexibility and innovation to the
- [What Counts as Extreme? Framing Weather Events for Grid Planning and Operation](https://gridforesight.ca/what-counts-as-extreme-framing-weather-events-for-grid-planning-and-operation/) - Heatwaves, cold snaps, tornadoes, earthquakes, are you hearing these terms in the news more often, or have personally experienced an impactful weather event? In 2025, Alberta alone faced some serious weather events that disrupted communities, forced evacuations, ravaged infrastructures, and left people without power. The power system is not immune from the threats of extreme
- [Quantum Computing and the Future of Power-System Optimisation: Promise, Hype, and the Road Ahead](https://gridforesight.ca/quantum-computing-and-the-future-of-power-system-optimisation-promise-hype-and-the-road-ahead/) - Quantum computing has been marketed as the next great computational revolution. A technology capable of reshaping materials science, leading to drug discovery, disrupting cryptography, helping in discovering new batteries, climate mitigation strategies, optimizing global energy systems [1-6], or supposedly cracking optimization problems that have resisted decades of classical computing, if you believe the brochures. Quantum
- [How Wildfire Smoke Disrupts Voltage Stability in PV-Rich Distribution Grids](https://gridforesight.ca/how-wildfire-smoke-disrupts-voltage-stability-in-pv-rich-distribution-grids/) - Wildfire smoke is no longer just an air-quality concern; it’s becoming a real operational factor for modern electric grids. As photovoltaic (PV) systems continue to integrate into distribution networks, feeder performance becomes increasingly dependent on weather and environmental conditions. Under clear skies, PV panels inject substantial power into the grid and reshape how voltages behave
- [Physics-Informed Neural Networks for Grid-Connected Batteries](https://gridforesight.ca/physics-informed-neural-networks-for-grid-connected-batteries/) - A Physics-Informed Neural Network (PINN) is a machine learning architecture designed to solve scientific problems by harmonizing data with physical laws. Its core innovation lies in training a neural network not only to fit observed data but also to adhere to known governing equations, typically ordinary or partial differential equations. This is achieved by incorporating
- [Agents for Optimizing Power Distribution Systems – Is this the Future? ](https://gridforesight.ca/agents-for-optimizing-power-distribution-systems-is-this-the-future/) - The Optimal Power Flow (OPF), as an optimization problem, is indispensable for the economic and secure operation of power distribution networks. The OPF is crucial for achieving a multitude of operational objectives including: efficient generation, transmission, and load expansion planning, mitigating power network losses, minimizing voltage deviations and maintaining voltage stability, and delivering on corporate
- [Emissions Response through Energy Storage System: A Path Toward Smarter, Cleaner Grid Operations](https://gridforesight.ca/emissions-response-through-energy-storage-system-a-path-toward-smarter-cleaner-grid-operations/) - Across the energy sector, storage has long been recognized as a cornerstone of grid reliability and renewable integration. Yet, the conversation about its environmental role often overlooks how they can actively contribute to carbon reduction. Our recent research, accepted for presentation at the Hawaii International Conference on System Sciences (HICSS) 2026, introduces a framework for
- [Designing for Flexibility: Navigating Imperfections in Competitive Electricity Markets](https://gridforesight.ca/designing-for-flexibility-navigating-imperfections-in-competitive-electricity-markets/) - Competitive electricity markets are inherently complex. They require the integration of technically feasible operations, grounded in physical laws, with economically viable solutions that incentivize investments. These interactions aim to ensure a reliable, efficient, and technologically flexible market design. However, the operation of these markets is neither trivial nor perfect. Over time, they are exposed to
- [Beyond Ownership - Unlocking the Potential of Shared Battery Storage](https://gridforesight.ca/beyond-ownership-unlocking-the-potential-of-shared-battery-storage/) - The emergence of the sharing economy has fundamentally altered paradigms of resource utilization, shifting the emphasis from individual ownership to collective access. This transformation, evident across domains such as transportation, housing, and digital infrastructure, is increasingly penetrating the energy sector. In particular, Battery Energy Storage Systems (BESS) are being reconceptualized as shared assets capable of
- [Long-term multi-resolution probabilistic load forecasting using temporal hierarchies](https://gridforesight.ca/long-term-multi-resolution-probabilistic-load-forecasting-using-temporal-hierarchies/) - This post summarizes our recent open-access publication in Energies.Read the full article here. Why Focus on Long-Term Load Forecasting? Long-term load forecasting (LTLF) is a crucial tool for planning and operating electric power systems. Accurate forecasts help system operators and planners make informed decisions about investments, operations, and policy. However, LTLF faces major challenges due
- [Beyond the Sunshine: How Real-World Conditions Shape Solar Panel Output](https://gridforesight.ca/beyond-the-sunshine-real-world-conditions-that-shape-solar-panel-output/) - Understand how environmental variables like temperature, humidity, aerosols and wind impact panel output - a critical foundation for deeper modeling & analysis.
- [Better forecasting in the headlines? Assessing the use of event analysis.](https://gridforesight.ca/better-forecasting-in-the-headlines-assessing-the-use-of-event-analysis/) - Introducing multi-modal input data could improve the performance of energy price and demand forecasting, leading to wider adoption of LLM-based forecasting
- [You know your content, but do you know your audience?](https://gridforesight.ca/you-know-your-content-but-do-you-know-your-audience/) - All presentations share a common goal of conveying content, but the most effective ones are tailored to the specific context and audience.
- [Weather Feature Selection for Robust and Optimized Energy Load Prediction](https://gridforesight.ca/weather-feature-selection-for-robust-and-optimized-energy-load-prediction/) - This study explores the impact of weather features on short to medium electricity load prediction across diverse geographical locations.
- [Better Transmission Loss Forecasting Using Large Language Models](https://gridforesight.ca/improving-transmission-loss-forecasting-using-large-language-models/) - Recent advancements in the field of Natural Language Processing (NLP) and Large Language Models (LLMs) have opened new avenues for processing qualitative data. This development is particularly interesting for power systems as they have an enormous amount of untapped qualitative data. Decision-making in the power system sector has relied on quantitative data. However, qualitative data
- [Effective Time Series Forecasting in the Age of Large Models: Insights from NeurIPS24](https://gridforesight.ca/time-series-forecasting-in-the-age-of-large-models-insights-from-neurips24/) - This post on forecasting explores transformers, the challenges of scaling and the rise of specialized foundation models like TTM, MOMENT, and Chronos.
- [Launch of Western Interconnected Grid](https://gridforesight.ca/launch-of-western-interconnected-grid/) - The centre's mission is to enhance the grid's resilience to the rising frequency, intensity, and duration of extreme weather events.
- [Eurasian Review, Shoring Up The West’s Grid Against Extreme Weather](https://gridforesight.ca/news-eurasian-review-shoring-up-the-wests-grid-against-extreme-weather/) - A new interdisciplinary center aimed at fortifying the western North America's power infrastructure against the floods, high winds, drought, even cold snaps.
- [Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study](https://gridforesight.ca/forecasting-the-occurrence-of-electricity-price-spikesa-statistical-economic-investigation-study/) - This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting.
- [Transition to Electrical Commercial Fleet](https://gridforesight.ca/paper-transition-to-electrical-commercial-fleet/) - Considering the purchase costs, salvage revenues, operating expenses, charging infrastructure costs and revenue potential of an EV fleet's electricity services.
- [Participation Models in Electricity Markets](https://gridforesight.ca/paper-participation-models-in-electricity-markets/) - Two market-participation models: in one, solar and energy storage submit separate offers. In the other, market operators treat the resource as a single unit.
- [Demand Charge Management](https://gridforesight.ca/paper-demand-charge-management/) - A new load management platform to help industrial and commercial electricity customers assess demand charge management through battery aggregation of EVs.
- [A Hybrid Imitation-Reinforcement Learning Framework for Optimal Operation of Soft Open Points in Unbalanced Distribution Networks](https://gridforesight.ca/paper-a-hybrid-imitation-reinforcement-learning-framework-for-optimal-operation-of-soft-open-points-in-unbalanced-distribution-networks/) - A hybrid deep actor-critic framework for the optimal operation of a phase-changing soft open point (PCSOP) in an unbalanced distribution network.
- [An Updated Review and Comparison of Wind Power Ramp Detection Techniques](https://gridforesight.ca/an-updated-review-and-comparison-of-wind-power-ramp-detection-techniques/) - The first step in analyzing and modeling wind power ramp events is detecting them. This work categorizes ramp detection techniques and compares their benefits.
- [A Deep Generative Model for Selecting Representative Periods in Renewable Energy-Integrated Power Systems](https://gridforesight.ca/paper-a-deep-generative-model-for-selecting-representative-periods-in-renewable-energy-integrated-power-systems/) - A new deep learning-based two-stage dataset-clustering/temporal-clustering method is proposed for time aggregation in renewable energy-integrated power systems.
- [AI-Assisted Physics-Based Model of Lithium-ion Battery for Power Systems Operation Research](https://gridforesight.ca/ai-assisted-physics-based-model-of-lithium-ion-battery-for-power-systems-operation-research/) - A new neural network-based model of a lithium-ion battery energy storage that replicates both degradation process and energy efficiency.
- [Emissions Response: Efficient Decarbonization using Real-Time Data](https://gridforesight.ca/emissions-response-efficient-decarbonization-using-real-time-data/) - Overview of real-time emissions factors as a metric toward grid efficiency with technologies that provide long-term stability and drive decarbonization.

## Pages

- [Meet the Grid Foresight Lab](https://gridforesight.ca/) - Tomorrow's Grid Today | Grid Foresight research: smart grids, AI, forecasting, optimization, battery energy storage systems, distributed energy systems.
- [Meet the Grid Foresight Lab's alumni over the years](https://gridforesight.ca/alumni/) - Many the many alumni - students, researchers and academics - who have contributed to and benefitted from their time with Grid Foresight.
- [Useful courses for the Lab](https://gridforesight.ca/relevant-courses/) - The following uCalgary engineering and data science courses are often taken by Grid Foresight Lab members at the undergraduate and graduate levels.
- [Meet our expert team of energy industry innovators](https://gridforesight.ca/team/) - We are a team of thinkers and innovators, led by Dr. Hamid Zareipour, charting new territories in power grid operations and energy systems research.
- [Innovative research for real-world applications](https://gridforesight.ca/research/) - Through research and industry collaborations, we harness the complexities of energy markets & emerging technologies to help shape the future of energy systems.
- [How to contact the Lab](https://gridforesight.ca/contact/) - Have questions about our research or opportunities to collaborate? Contact us to discuss how we can drive real-world, innovative energy solutions together.
- [How to join the Lab](https://gridforesight.ca/join-the-team/) - Here's what you need to know to join us at the Grid Foresight Lab. Our Lab director, Dr. Hamid Zareipour, reviews all applications.
- [How to join the Lab](https://gridforesight.ca/join-the-lab/) - Our team specializes in developing data-driven models and solutions that support the next generation of sustainable, resilient power systems. Join us!
- [Publications & News](https://gridforesight.ca/publications/) - These are the most recent journal articles, blogs publications & news from our team. For all papers by Dr. Zareipour visit his Google Scholar listing.

## Team

- [Austyn Nagribianko](https://gridforesight.ca/dt_team/austyn-nagribianko/) - M.Sc. Student
- [Alireza Esmaeili](https://gridforesight.ca/dt_team/alireza-esmaeili/) - M.Sc. Student
- [Dr. Hamid Zareipour](https://gridforesight.ca/dt_team/dr-hamidreza-hamid-zareipour/) - Professor, P.Eng, FIEEE
- [Pardis Abbasnejad](https://gridforesight.ca/dt_team/pardis-abbasnejad/) - Undergraduate Student
- [Maya Stuart](https://gridforesight.ca/dt_team/maya-stuart/) - M.Sc. Student
- [Sana Sajjad](https://gridforesight.ca/dt_team/sana-sajjad/) - Undergraduate Student
- [Daniel Walton](https://gridforesight.ca/dt_team/daniel-walton/) - M.Sc. Student
- [Samuel Bakker](https://gridforesight.ca/dt_team/samuel-bakker/) - M. Sc. Student
- [Dr. Haotian Yao](https://gridforesight.ca/dt_team/haotian-yao/) - Post Doctoral Fellow
- [Mohammad Alhashem](https://gridforesight.ca/dt_team/mohammad-alhashem/) - Ph.D. Student
- [Yuhao Huang](https://gridforesight.ca/dt_team/yuhao-huang/) - Ph.D. Student
- [Dr. Fatemeh Shakeri](https://gridforesight.ca/dt_team/fatemeh-shakeri/) - Post Doctoral Fellow
- [Ala'a Al-Sharif](https://gridforesight.ca/dt_team/alaa-al-sharif/) - Ph.D. Student
- [Sherry Gao](https://gridforesight.ca/dt_team/sherry-gao/) - M. Sc. Student
- [Shafie Bahman](https://gridforesight.ca/dt_team/shafie-bahman/) - Ph.D. Student
- [Md Rafiu Hossain](https://gridforesight.ca/dt_team/md-rafiu-hossain/) - Undergraduate Student
- [Vivian Tat](https://gridforesight.ca/dt_team/vivian-tat/) - Undergraduate Student
- [Colby Valcourt](https://gridforesight.ca/dt_team/colby-valcourt/) - M.Sc. Student
- [Amirhossein Ahmadi](https://gridforesight.ca/dt_team/amirhossein-ahmadi/) - Ph.D. Student
- [Manuel Zamudio López](https://gridforesight.ca/dt_team/manuel-zamudio-lopez/) - Ph.D. Student
- [Gideon Egharevba](https://gridforesight.ca/dt_team/gideon-egharevba/) - Ph.D. Student
- [Ahmed Al-Shafei](https://gridforesight.ca/dt_team/ahmed-al-shafei/) - Ph.D. Student
- [Abhinav Ayri](https://gridforesight.ca/dt_team/abhinav-ayri/) - M.Sc. Student (part-time)
- [Rafael Medeiros](https://gridforesight.ca/dt_team/rafael-medeiros/) - Research Associates
- [Ellie Salimi](https://gridforesight.ca/dt_team/ellie-salimi/) - Ph.D. Student
- [Mohsen Tavakolian](https://gridforesight.ca/dt_team/mohsen-tavakolian/) - Ph.D. Student
- [Suleman Osman](https://gridforesight.ca/dt_team/suleman-osman/) - M.Sc. Student
- [Muhammad Mutahhar Chishti](https://gridforesight.ca/dt_team/muhammad-mutahhar-chishti/) - M.Sc. Student
- [Vahid Hakimian](https://gridforesight.ca/dt_team/vahid-hakimian/) - Ph.D. Student
- [Sean (Shahin) Parvar](https://gridforesight.ca/dt_team/sean-parvar/) - Ph.D. Candidate
- [Chibuike Peter Ohanu](https://gridforesight.ca/dt_team/chibuike-peter-ohanu/) - Ph.D. Student
- [Jose Carmo](https://gridforesight.ca/dt_team/jose-carmo/) - M.Sc. Student
- [Shirin Yamani](https://gridforesight.ca/dt_team/shirin-yamani/) - Research Assistant
- [Shoaib Hussain](https://gridforesight.ca/dt_team/shoaib-hussain/) - Ph.D. Student
- [Hossein Karimi](https://gridforesight.ca/dt_team/hossein-karimi/) - Ph.D. Student
- [Matt Tierney](https://gridforesight.ca/dt_team/matt-tierney/) - Ph.D. Student
- [Ali Forootani](https://gridforesight.ca/dt_team/2333/) - Ph.D. Student

## Categories

- [Journal article](https://gridforesight.ca/category/journal/)
- [Conference article](https://gridforesight.ca/category/conference/)
- [Blog](https://gridforesight.ca/category/blog/)
- [In the news](https://gridforesight.ca/category/news/)

## Tags

- [power systems](https://gridforesight.ca/tag/power-systems/)
- [renewable energy](https://gridforesight.ca/tag/renewable-energy/)
- [energy transition](https://gridforesight.ca/tag/energy-transition/)
- [grid transformation](https://gridforesight.ca/tag/grid-transformation/)
- [energy storage](https://gridforesight.ca/tag/energy-storage/)
- [smart grid](https://gridforesight.ca/tag/smart-grid/)
- [power system operation](https://gridforesight.ca/tag/power-system-operation/)
- [power system planning](https://gridforesight.ca/tag/power-system-planning/)
- [energy forecasting](https://gridforesight.ca/tag/energy-forecasting/)
- [commercial fleet](https://gridforesight.ca/tag/commercial-fleet/)
- [fleet transition](https://gridforesight.ca/tag/fleet-transition/)
- [electric bus](https://gridforesight.ca/tag/electric-bus/)
- [electric fleet](https://gridforesight.ca/tag/electric-fleet/)
- [aggregated battery](https://gridforesight.ca/tag/aggregated-battery/)
- [charging infrastructure](https://gridforesight.ca/tag/charging-infrastructure/)
- [grid ancillary services](https://gridforesight.ca/tag/grid-ancillary-services/)
- [pcsop](https://gridforesight.ca/tag/pcsop/)
- [unbalanced distribution network](https://gridforesight.ca/tag/unbalanced-distribution-network/)
- [reinforcement learning](https://gridforesight.ca/tag/reinforcement-learning/)
- [power loss](https://gridforesight.ca/tag/power-loss/)
- [nonlinear ac optimal power flow](https://gridforesight.ca/tag/nonlinear-ac-optimal-power-flow/)
- [clustering](https://gridforesight.ca/tag/clustering/)
- [GAN](https://gridforesight.ca/tag/gan/)
- [LSTM](https://gridforesight.ca/tag/lstm/)
- [time aggregation](https://gridforesight.ca/tag/time-aggregation/)
- [probability dostribution learning](https://gridforesight.ca/tag/probability-dostribution-learning/)
- [sequence learning](https://gridforesight.ca/tag/sequence-learning/)
- [temporal diversity patterns](https://gridforesight.ca/tag/temporal-diversity-patterns/)
- [spatial diversity patterns of renewables](https://gridforesight.ca/tag/spatial-diversity-patterns-of-renewables/)
- [Electricity Price](https://gridforesight.ca/tag/electricity-price/)
- [AI in energy forecasting](https://gridforesight.ca/tag/ai-in-energy-forecasting/)
- [energy Price Spike Predictions](https://gridforesight.ca/tag/energy-price-spike-predictions/)
- [energy capacity](https://gridforesight.ca/tag/energy-capacity/)
- [ai](https://gridforesight.ca/tag/ai/)
- [energy resevoir](https://gridforesight.ca/tag/energy-resevoir/)
- [dispatch](https://gridforesight.ca/tag/dispatch/)
- [lithium ion battery](https://gridforesight.ca/tag/lithium-ion-battery/)
- [battery degradation](https://gridforesight.ca/tag/battery-degradation/)
- [neural network](https://gridforesight.ca/tag/neural-network/)
- [energy arbitrage](https://gridforesight.ca/tag/energy-arbitrage/)
- [higher revenue](https://gridforesight.ca/tag/higher-revenue/)
- [wind power](https://gridforesight.ca/tag/wind-power/)
- [wind turbine](https://gridforesight.ca/tag/wind-turbine/)
- [power grid](https://gridforesight.ca/tag/power-grid/)
- [grid](https://gridforesight.ca/tag/grid/)
- [wind energy](https://gridforesight.ca/tag/wind-energy/)
- [clean energy](https://gridforesight.ca/tag/clean-energy/)
- [wind power ramp](https://gridforesight.ca/tag/wind-power-ramp/)
- [wind ramp](https://gridforesight.ca/tag/wind-ramp/)
- [intermittency](https://gridforesight.ca/tag/intermittency/)
- [Decarbonization](https://gridforesight.ca/tag/decarbonization/)
- [Peak Demand](https://gridforesight.ca/tag/peak-demand/)
- [emissions](https://gridforesight.ca/tag/emissions/)
- [dynamic response](https://gridforesight.ca/tag/dynamic-response/)
- [system efficiency](https://gridforesight.ca/tag/system-efficiency/)
- [time-sensitive consumption](https://gridforesight.ca/tag/time-sensitive-consumption/)
- [low-emissions technologies](https://gridforesight.ca/tag/low-emissions-technologies/)
- [grid efficiency](https://gridforesight.ca/tag/grid-efficiency/)
- [long-term stability](https://gridforesight.ca/tag/long-term-stability/)
- [Time Series](https://gridforesight.ca/tag/time-series/)
- [academic presentations](https://gridforesight.ca/tag/academic-presentations/)
- [lecture](https://gridforesight.ca/tag/lecture/)
- [these defence](https://gridforesight.ca/tag/these-defence/)
- [thesis defense](https://gridforesight.ca/tag/thesis-defense/)
- [powerpoint](https://gridforesight.ca/tag/powerpoint/)
- [conference talk](https://gridforesight.ca/tag/conference-talk/)
- [speaker](https://gridforesight.ca/tag/speaker/)
- [seminar](https://gridforesight.ca/tag/seminar/)
- [research presentation](https://gridforesight.ca/tag/research-presentation/)
- [feature selection](https://gridforesight.ca/tag/feature-selection/)
- [weather parameters](https://gridforesight.ca/tag/weather-parameters/)
- [Temporal models](https://gridforesight.ca/tag/temporal-models/)
- [Machine Learning](https://gridforesight.ca/tag/machine-learning/)
- [Artificial Neural Network](https://gridforesight.ca/tag/artificial-neural-network/)
- [large language models](https://gridforesight.ca/tag/large-language-models/)
- [natural language processing](https://gridforesight.ca/tag/natural-language-processing/)
- [LLM](https://gridforesight.ca/tag/llm/)
- [NLP](https://gridforesight.ca/tag/nlp/)
- [forecasting](https://gridforesight.ca/tag/forecasting/)
- [solar](https://gridforesight.ca/tag/solar/)
- [renewable](https://gridforesight.ca/tag/renewable/)
- [modeling](https://gridforesight.ca/tag/modeling/)
- [weather](https://gridforesight.ca/tag/weather/)
- [Sharing_Economy](https://gridforesight.ca/tag/sharing_economy/)
- [ESaaS](https://gridforesight.ca/tag/esaas/)
- [electricity markets](https://gridforesight.ca/tag/electricity-markets/)
- [electricity prices](https://gridforesight.ca/tag/electricity-prices/)
- [agents](https://gridforesight.ca/tag/agents/)
- [smart grids](https://gridforesight.ca/tag/smart-grids/)
- [power distribution](https://gridforesight.ca/tag/power-distribution/)
- [power sysem optimization](https://gridforesight.ca/tag/power-sysem-optimization/)
- [quantum computing](https://gridforesight.ca/tag/quantum-computing/)
- [extreme events](https://gridforesight.ca/tag/extreme-events/)
- [Energy storage as a service](https://gridforesight.ca/tag/energy-storage-as-a-service/)
- [SATOA](https://gridforesight.ca/tag/satoa/)
- [Data Center](https://gridforesight.ca/tag/data-center/)
- [load forecasting](https://gridforesight.ca/tag/load-forecasting/)
- [load modeling](https://gridforesight.ca/tag/load-modeling/)
- [Electric vehicle](https://gridforesight.ca/tag/electric-vehicle/)
- [Cold-Start Forecasting](https://gridforesight.ca/tag/cold-start-forecasting/)
- [Transfer Learning](https://gridforesight.ca/tag/transfer-learning/)
- [Generative models](https://gridforesight.ca/tag/generative-models/)
- [Distribution Planning](https://gridforesight.ca/tag/distribution-planning/)
- [Non-Wires Alternatives](https://gridforesight.ca/tag/non-wires-alternatives/)
- [Battery Energy Storage](https://gridforesight.ca/tag/battery-energy-storage/)
- [Utility Planning](https://gridforesight.ca/tag/utility-planning/)

## Team Categories

- [Current Members](https://gridforesight.ca/dt_team_category/current-members/)
- [Alumni](https://gridforesight.ca/dt_team_category/alumni/)
- [Masters students](https://gridforesight.ca/dt_team_category/current-members/masters-students/)
- [PhD students](https://gridforesight.ca/dt_team_category/current-members/phd-students/)
- [Post doctoral fellow](https://gridforesight.ca/dt_team_category/current-members/post-graduates-and-researchers/)
- [Leadership](https://gridforesight.ca/dt_team_category/current-members/leadership/) - Leadership
- [Undergraduate students](https://gridforesight.ca/dt_team_category/current-members/ug-students/)
- [Research Associates](https://gridforesight.ca/dt_team_category/current-members/research-associates/)