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TransitionZero

A brief introduction to Scenario Builder, our free, no-code energy system modelling platform 

What is Scenario Builder?

Get Started

This section outlines the modelling methodology behind Scenario Builder. Our goal is to make energy system modelling accessible by being transparent about the data, assumptions, and calculations we use - even for those without a technical background. This document is a key part of your learning journey. It's a living resource, updated as the platform evolves and shaped by valuable user feedback.

Model methodology

This page explains how spatial (geographic) and temporal (time) resolution impacts modelling results. 

Model resolution

This page discusses how Scenario Builder uses existing asset data, an essential starting input for energy system modelling.

Existing assets

This page describes input data for future assets, or what could be built in the future.

Future assets

This page discusses the different types of tecno-economic input data used in energy system modelling

Techno-economic inputs

This page discusses commodity prices, an essential data input for energy system models.

Commodity (fuel) prices

This page discusses how financial data inputs influence a model

Financial inputs

What are RE potentials and how do they impact modelling results?

Renewable energy potentials

What are RE profiles, and how do they impact modelling results?

Renewable energy profiles

Policies and targets

This section outlines the methodology and input data used to develop demand projections within Scenario Builder.

Demand

What are electricity demand profiles, and how do they impact model results?

Demand profiles

This section provides specific details about Scenario Builder's Indonesia model

Indonesia

This section provides specific details about Scenario Builder's model of the Maldives.

Maldives

This section provides specific details about Scenario Builder's model of Samoa.

Samoa

Model Calibration

Welcome to the home of your new documentation. Now testing

TranstionZero

Follow the steps below to start making data forecasts with MindsDB using standard SQL.

Quickstart

MindsDB enables you to automate AI workflows and get real-time predictions utilizing all available data.

Automation

AI Workflow, as defined by MindsDB, comprises steps that include fetching data from a user-defined data source, deploying a production-ready AI model, and, if required, training it with data.

AI Workflow

At the heart of the MindsDB philosophy lies the belief that predictive insights are best leveraged when produced as close as possible to the data layer.

Handlers

MindsDB provides various data and app handlers that let you connect virtually any data source and make use of your data to finetune models or make predictions.

Data Sources

MindsDB offers a wide range of AI engines used to create models and incorporate them in the data landscape as virtual tables. MindsDB abstracts AI models as virtual tables, or Generative AI Tables, that can generate data from the underlying model upon being queried.

ML Engines

MindsDB empowers organizations to harness the power of AI by abstracting AI models as Generative AI Tables.

Generative AI Tables

In this section, we go through the AI models types available in MindsDB. These are regression models, classification models, time series models, and large language models (LLMs).

Models

Callbacks enable the retrieval of a model's status when it transitions to either 'complete' or 'error'.

Callbacks

Tables, views, and files store data. Let's go over these concepts.

Platform

Methodology

Country Models

Feedback

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