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Energy4cast challenge 2023

Introduction

Welcome to the Energy4cast challenge 2023! This competition is designed for data science enthusiasts, PhD students, and junior professionals who are passionate about energy optimization and eager to contribute to sustainable practices. By participating in this challenge, you will have the opportunity to make a meaningful impact on energy consumption patterns for a university campus in Brazil.

Introduction

Background

As the global shift towards sustainable energy resources continues, the forecasting accuracy of electricity demand becomes increasingly important for utilities and grid operators. Accurate predictions enable better planning for power production and distribution, as well as optimization of pricing and balancing of supply and demand.

Electricity demand forecasting is a complex task that can be impacted by a wide range of factors such as weather, electricity prices, and consumer behaviors. To address this challenge, a variety of methods have been employed, including statistical methods and machine learning algorithms, with an increasing focus on deep learning models as the amount of available data and computational power has grown.

However, the increasing complexity in the energy sector with the integration of distributed energy resources and various technologies introduces new challenges for load forecasting. Furthermore, the specific purpose of the forecast, whether it be for short-term, long-term, or peak load forecasting, also presents unique challenges. Moreover, different sources of electricity load, such as private households, industries, or office buildings, require different supporting data and present additional conditions that further complicate the accuracy of an electricity load forecast.

Background

Problem statement

The main goal of this competition is to develop a machine learning model that can optimize energy consumption across the university campus by analyzing historical energy consumption data, roof-top solar energy production data, electric bus battery data, and weather data. The campus comprises multiple buildings and facilities, and the model should consider the unique energy requirements and characteristics of each.

 

Objectives:

  • Develop a forecasting model to predict the energy consumption of each building on the campus over the course of the day.

  • Optimize the usage of solar energy by analyzing the solar energy production data and weather data to identify patterns and predict solar energy generation.

  • Incorporate electric bus battery data to optimize the campus transportation system's energy consumption and charging schedules.

  • Identify energy efficiency opportunities and potential areas for improvement across the campus by analyzing the historical data and highlighting irregularities or inefficiencies in energy consumption patterns.

  • Create a user-friendly visualization tool that presents the optimization strategies and recommendations to stakeholders in an easily understandable format.

Problem statement

Dataset

The dataset provided for the Campus Energy Load Optimization Challenge consists of various data sources collected from a university campus in Brazil over a period of approximately two years. The dataset is structured into five main categories:

  1. Energy Consumption Data

  2. Solar Energy Production Data

  3. Electric Bus Battery Data

  4. Weather Data

  5. Campus Building Metadata

 

The detailed description of the dataset will be available soon

Dataset

Evaluation Criteria

Coming soon!

Evalution Criteria

Submission

Coming soon!

Submission

Timeline

Phase I (Warm-Up Round, 11 December 2023 - 15 January 2024)

Phase II (Validation Round, 15 January – 14 April 2024)

Phase III (Evaluation and Winner Round, 14 April – 20 May 2024)

Details will come soon!

Timeline

Prizes

Coming soon!

Prizes

Rules

To ensure that the competition is conducted in a fair and ethical manner, and that all participants should hold to a high standard of conduct:

  • Respect: Participants are expected to treat others with respect, regardless of their background, identity, or affiliation.

  • Honesty: Participants are expected to be honest and truthful in their submissions, and not to plagiarize or copy the work of others.

  • Integrity: Participants are expected to maintain the integrity of the competition and not engage in any activity that could compromise the fairness or accuracy of the results.

  • Confidentiality: Participants are expected to keep the data provided for the competition confidential and not to share it with others.

  • Legal Compliance: Participants are expected to comply with all applicable laws, regulations, and ethical standards.

  • Consequences: Violations of the Code of Conduct may result in disqualification or other sanctions, as determined by the competition organizers.

Rules

Organizers

Organizers
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