Using AI to forecast energy consumption

Using AI to forecast energy consumption

The global awareness for a more sustainable future has been growing at a rapid rate and this, in turn, led to the same swift increase in renewable energy generation.

This is especially noticeable in countries such as Germany, the United Kingdom, Sweden, Spain, Italy, Brazil, etc., which are the leading countries in terms of the highest proportion of renewable energy.

However, even if you are not in these countries, seeing solar panels and wind turbines should not be news to you, as their practices have become widespread around the world.

And while the idea of renewable energy is appealing, it faces certain challenges, challenges that have become one of the top priorities to address in the energy sector. Mainly, how to balance energy production and consumption.

Balancing supply and demand in the power system 

Why did energy production and consumption become so important? Well, to be more precise, balancing the supply and demand for electricity has always been and will always be a priority for the sector due to the nature of electricity itself.

Unfortunately, electricity is not like other commodities and it is extremely expensive to store in large quantities. Therefore, there has always been an interest in balancing its use and generation.

Finding the equilibrium between the two is important for everyone who is dependent on electricity, however, it is especially a trending topic for: 

  • The power generation companies – companies that transform different forms of energy (solar, hydro, wind, fossil, nuclear, etc.) into electricity.
  • The Balance Responsible Parties (BRP’s) – Participant in the electricity market that is in charge of balancing the supply and demand of electricity and planning the daily transactions of the electricity network administrations.

To fully understand how the energy market works, let’s take a look at the energy trading process.

Electricity market

As a market for different products, the electricity market is a system in which many participants sell and buy electricity at a competitive price. Ideally, the market provides reliable electricity to consumers at a minimal cost.

However, there are certain conditions that apply to the market. 

Capacity Market (CM)

Capacity Market is a mechanism that guarantees compensation to power plants for capacity, or electricity delivered in the future. 

Let’s take a look at an example to clarify this. If a customer asks an electricity supplier for 100 megawatts of electricity and will only use 70 megawatts, the consumer is still obligated to pay the supplier for 30 megawatts not used.

Why is this so? This agreement ensures that consumers do not abuse the process and do not order an excessive amount of electricity for the scenario of “just in case.”

With the obligation to pay the extra amount, the market is balanced and ensures that customers order the exact amount necessary so that suppliers do not produce unnecessary amounts of electricity, which is a financial loss for them.

Day-Ahead Market

The day-ahead market allows energy traders to buy or sell electricity one day before the operating day. This process lets buyers and sellers trade and lock in prices a day ahead, dodging price volatility.

Intraday Market

Unlike the day-ahead market, in the intraday market, the negotiation is carried out at energy exchange prices that are valid for the same day of energy delivery. 

When it comes to these two markets, there is no case of mutual exclusivity. To ensure the necessary balance between electricity supply and demand, the intraday market often works together with the day-ahead market. However, the prices in the intraday market are much higher as it is a last-minute order.

AI-powered forecasting- The missing piece 

AI-powered energy forecasting

By looking at these conditions mentioned above, we can clearly see that for the smooth management of supply and demand from all market participants, it is vital to forecast energy demand and generation.

Artificial intelligence is a tool that has been able to fill this information gap with different methods. Let’s look at some of the most common ways that AI predicts energy consumption and generation.

Time series 

The time series is a sequence of data in consecutive order and is used for forecasting future instances based on that observational data. 

For our case, the time series is an important aspect for the prediction of the power generation potential based on meteorological data. 

By analyzing historical meteorological data related to humidity, wind speed and direction, cloud cover, solar irradiance, etc., we can forecast what is the potential electricity production of renewable energy devices.

Time series are also used to predict what the electricity demand will be based on the specific time period such as weekends, holidays, weekdays, etc. as consumption tends to vary in these periods.

Artificial neural networks (ANN’s)

Neural networks are machine learning algorithms that are powered on data and are inspired by the way the human brain works.

The way the neural network works is that it feeds on input data which then runs continuously with interconnected nodes similar to the human brain, recognizing patterns and correlations of raw data and producing the output data.

The neural network is a widely used method of forecasting time series. Depending on the weather or period data, the neural network will recognize patterns and understand during which days the demand for electricity is highest or how much energy can be produced in a particular season.

Conclusion

After taking a look at the electricity market, we see how important it is to balance the supply and demand for power generation. 

Fortunately, with a wealth of accumulated data and modern artificial intelligence methods, members of these markets can predict electricity generation potential or demand and avoid overpriced electricity trading or paying fines.

MaxinAI is made up of leading experts with more than 30 years of experience in the energy sector. Our team has delivered to clients from around the world with 24-hour forecast models with a mean absolute percentage error (MAPE) of 92%.

If you also want to take advantage of innovative tools and predict power generation potential or demand, our experts offer a free consultation call to better understand your specific use case and help us inform you of the necessary steps of our partnership.

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© 2021 - MaxinAI | All Rights Reserved
© 2021 - MaxinAI | All Rights Reserved