EnergyAPI: An End-to-End API for Energy-Aware Forecasting and Scheduling

Fri, 30 Jan 2026 00:00:00 +0000

Over the past decades, electricity demand has increased steadily, driven by structural shifts such as the electrification of transportation and, more recently, the rapid expansion of artificial intelligence (AI). Power grids have responded by expanding generation capacity, integrating renewable energy sources such as solar and wind, and deploying demand-response mechanisms. However, the current pace of demand growth is increasingly outstripping grid expansion, leading to integration delays, greater reliance on behind-the-meter consumption, and rising operational complexity.

To mitigate the environmental and socioeconomic impacts of electricity consumption, large consumers such as cloud data centers and electric vehicle (EV) charging infrastructures are increasingly participating in demand-response programs. These programs provide consumers with grid signals indicating favorable periods for electricity usage, such as when energy is cheapest or has the lowest carbon intensity. Consumers can then shift workloads across time and location to better align with grid conditions and their own operational constraints. A key challenge, however, is the online nature of this problem: operators must make real-time decisions without full knowledge of future grid conditions. While forecasting and optimization techniques exist, their effectiveness depends heavily on workload characteristics, such as whether tasks are delay-tolerant cloud jobs or EV charging sessions with route and deadline constraints.

This project proposes the design and implementation of a modular, extensible API for energy-aware workload scheduling. The API will ingest grid signals alongside workload Service Level Objectives (SLOs) and operational requirements, and produce execution plans that adapt to changing grid conditions. It will support multiple pluggable scheduling strategies and heuristics, enabling developers to compare real-time and forecast-based approaches across different workload classes. By providing a reusable, open-source interface for demand-response-aware scheduling, this project aims to lower the barrier for developers to integrate energy-aware decision-making into distributed systems and applications.

Building an End-to-End Service for Energy Forecasting and Scheduling

Topics: Databases Machine Learning
Skills: Python, command line tools (bash), SQL (MySQL or SQLite), FastAPI, time-series analysis, basic machine learning
Difficulty: Moderate
Size: Large (350 hours)
Mentors: Abel Souza

Develop a containerized, end-to-end platform consisting of a backend, API, and web-based frontend for collecting, estimating, and visualizing real-time and forecasted electrical grid signals. These signals include electricity demand, prices, energy production, grid saturation, and carbon intensity. The system will support scalable data ingestion, region-specific forecasting models, and interactive visualizations to enable energy-aware application development and analysis.

Tasks:

Study electrical grid signals and demand-response data sources (e.g., demand, price, carbon intensity, grid saturation) and identify their requirements for real-time and forecast-based consumption planning.
Design and implement a relational data model for storing historical, real-time, and forecasted grid signals.
Ingest and validate grid signal data into a MySQL or SQLite database, ensuring data quality and time alignment across regions.
Implement baseline time-series forecasting models for grid signals (e.g., demand, price, or carbon intensity), with support for region-specific configurations.
Query European Network of Transmission System Operators for Electricity (ENTSO-E) and EIA (Energy Information Administration (EIA)) APIs to collect grid data.
Develop a RESTful API that exposes both raw and forecasted grid signals for use by energy-aware applications and schedulers.
Build a web-based user interface to visualize historical trends, forecasts, and regional differences in grid conditions.
Implement an interactive choropleth map to display spatial variations in grid signals such as carbon intensity and electricity prices.
Design an extensible architecture that allows different regions to plug in custom forecasting models or heuristics.
Containerize the backend, API, and frontend components using Docker to enable reproducible deployment and easy integration by external users.

CarbonCast: Building an end-to-end consumption-based Carbon Intensity Forecasting service

Tue, 18 Feb 2025 00:00:00 +0000

CarbonCast is a machine-learning-based approach to provide multi-day forecasts of the electrical grid’s carbon intensity. Developed in Python, the current version of CarbonCast delivers accurate forecasts in numerous regions by using historical source production data of a particular geographical region, time of day/year, and weather forecasts as features. However, there is no easy way to access and visualize the data through a standard interface. In addition, much important information is left out and is not available to users. For instance, electricity grids often import electricity from neighboring regions and so electricity consumption depends on both electricity generation and imports. Moreover, it is imperative for each energy source to utilize a tailored predictive mechanism. Consequently, any carbon optimization solution trying to reduce carbon emissions due to its electricity consumption will benefit more from following a consumption-based CI signal.

The plan for this project is to develop both the frontend and the backend API services for CarbonCast. We also intend to enhance CarbonCast by implementing an architecture wherein each region can employ a distinct interface for their predictive modeling. In scenarios where these new models do not yield superior outcomes within a region, the current architecture will serve as a fallback solution.

Building an end-to-end consumption-based Carbon Intensity Forecasting service

Topics: Databases Machine Learning
Skills: Python, command line (bash), MySQL, Django, machine learning, cronjob
Difficulty: Moderate
Size: Medium (175 hours)
Mentors: Abel Souza

Develop a containerized end-to-end backend, API, and frontend for collecting, estimating, and visualizing real-time and forecast electrical grid’s carbon intensity data in a scalable manner.

Tasks:

Research web technologies and frameworks relevant to CarbonCast development.
Run and collect CarbonCast’s data (CSV)
Ingest CSV into a MySQL or SQLite database
Develop an Application Programming Interface (API) and a Web User Interface (UI) to provide real-time data access and visualization.
Deploy the CarbonCast API as a service and dockerize it so that other users and applications can locally deploy and use it easily.
Implement a choropleth web map to visualize the carbon intensity data across the different geographical regions supported by CarbonCast.
Enhance CarbonCast by implementing an extensible architecture wherein every region can employ distinct models for their predictive modeling.

Abel Souza | UCSC OSPO

EnergyAPI: An End-to-End API for Energy-Aware Forecasting and Scheduling

Building an End-to-End Service for Energy Forecasting and Scheduling

CarbonCast: Building an end-to-end consumption-based Carbon Intensity Forecasting service

Building an end-to-end consumption-based Carbon Intensity Forecasting service