How do you model and forecast energy in your practice?

1 Energy modeling

Energy modeling is the process of creating a mathematical representation of the physical and economic aspects of energy systems, such as power plants, grids, buildings, vehicles, and markets. Energy models can help you analyze the current and future behavior of energy systems, evaluate different scenarios and options, and identify potential risks and opportunities. Energy models can vary in scope, scale, complexity, and purpose, depending on your objectives and data availability. Some examples of energy modeling tools are:

- EnergyPLAN : A software that simulates the operation of national or regional energy systems, with a focus on renewable energy integration and energy efficiency.

- LEAP : A software that enables long-term energy planning and policy analysis, with a comprehensive accounting of environmental and social impacts.

- RETScreen : A software that helps you assess the technical and financial feasibility of renewable energy and energy efficiency projects.

2 Energy forecasting

Energy forecasting is the process of estimating the future demand and supply of energy, as well as the associated prices and costs, based on historical data, trends, and assumptions. Energy forecasting can help you plan and manage your energy resources, budget, and investments, as well as anticipate and respond to market fluctuations and uncertainties. Energy forecasting can be done at different time horizons, from short-term (hours to days) to long-term (years to decades), and at different levels of granularity, from aggregate (national or regional) to disaggregate (sectoral or individual). Some examples of energy forecasting methods are:

- Regression : A statistical technique that uses historical data to establish a relationship between one or more explanatory variables (such as weather, income, or population) and an outcome variable (such as energy demand or price).

- Time series : A statistical technique that uses historical data to capture the patterns and trends of a variable over time (such as seasonality, cycles, or trends) and extrapolate them into the future.

- Machine learning : A computational technique that uses algorithms to learn from data and make predictions based on complex and nonlinear relationships.

3 Energy data

Energy data is the foundation of energy modeling and forecasting. Without reliable, accurate, and timely data, you cannot build or validate your models or forecasts, or communicate your results and insights. Energy data can come from various sources, such as:

- Official statistics : Data collected and published by national or international organizations, such as the International Energy Agency (IEA), the U.S. Energy Information Administration (EIA), or the World Bank.

- Market data : Data generated and traded by energy markets, such as spot prices, futures contracts, or bids and offers.

- Sensor data : Data measured and transmitted by devices, such as smart meters, satellites, or drones.

4 Energy visualization

Energy visualization is the process of presenting and communicating your energy data, models, and forecasts in a clear, engaging, and interactive way. Energy visualization can help you explore and understand your data, models, and forecasts, as well as share your findings and recommendations with your stakeholders and audiences. Energy visualization can use various formats and techniques, such as:

- Charts : Graphical representations of data, such as line charts, bar charts, pie charts, or scatter plots.

- Dashboards : Collections of charts and indicators that display key information and metrics, such as energy consumption, cost, or emissions.

- Maps : Geographical representations of data, such as heat maps, choropleth maps, or network maps.

5 Energy skills

Energy skills are the competencies and abilities that you need to model and forecast energy in your practice. Energy skills can be divided into three categories:

- Technical skills : The knowledge and expertise that you need to use the tools and methods of energy modeling and forecasting, such as mathematics, statistics, programming, or engineering.

- Business skills : The knowledge and expertise that you need to understand the context and objectives of your energy modeling and forecasting, such as economics, finance, or management.

- Soft skills : The knowledge and expertise that you need to communicate and collaborate effectively with your clients, colleagues, and partners, such as presentation, negotiation, or teamwork.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?