How do you use AI to improve load forecasting accuracy and reliability?

AI enhances load forecasting accuracy and reliability by processing vast and diverse datasets from sources like smart meters and weather stations. It identifies patterns and trends hidden in historical data, offering insights conventional methods might miss. AI adapts to changing conditions, updating models dynamically for more accurate forecasts. It also generates probabilistic forecasts, quantifying uncertainties and risks associated with load outcomes, improving decision-making for utilities and grid operators.

AI algorithms, such as machine learning (ML) and deep learning (DL), can analyze historical load data along with various factors influencing electricity demand, such as weather patterns, economic indicators, and demographic trends. These models can identify complex patterns and relationships in the data to make more accurate load forecasts. the most important part is to gather the relevant data , because in this era, you can rule by data.

AI can significantly enhance the accuracy of load forecasting, provided that the input data is of high quality. Accurate load forecasting relies heavily on precise weather predictions, as weather conditions directly affect energy consumption and generation. For instance, increased sunlight boosts solar production, reducing the load on the system, while lower temperatures lead to higher heating demand and increased load. Therefore, if weather forecasts are inaccurate, even the most sophisticated machine learning models will not outperform basic regression models.

In our project, we found that time series model like SARIMA combined with regression model of Gradient boosting have performed well for load forecasting using Machine Learning. Some of the factors that we considered to perform load forecasting were weather data (Temperature, Humidity, Pressure, Global Horizontal etc.), Time of the day, Previous day load, Previous week load. Seasonality of data is captured using SARIMA time series model.

Load forecasting can be categorised into two types: one where predictions do not influence the outcome (e.g., weather) and another where they do (e.g., stock market). Load forecasting tends to align more with the first type. However, in a liberalised energy market, it leans towards the latter as major energy-consuming industries forecast and adjust production based on projected prices, introducing additional uncertainty. In the Western Australian market, simple time series models like ARIMA and SARIMAX suffice for accurate load forecasting. In more liberalised markets with higher uncertainty, advanced models like LSTM could be advantageous.

Utilities and energy companies use load forecasting to plan and optimize their generation, transmission, and distribution infrastructure. By forecasting future demand, they can make informed decisions about capacity expansion, infrastructure upgrades, and investment in renewable energy sources.

ARIMA and SARIMAX models are highly effective for short-term load forecasting, such as half-hourly predictions. These models primarily use historical load and weather data as inputs, making the process straightforward and efficient. Their ability to handle seasonality and trends in the data makes them suitable for routine load forecasting tasks in many contexts.

In a centralised market, accurate load forecasting enables system operators to dispatch generators more efficiently and at lower costs. In a liberalised market, AI offers significant benefits to energy producers, retailers, and large consumers with access to the wholesale market. System operators also benefit from reduced overutilisation of essential system services (ESS). Battery asset owners stand to gain the most, as precise load forecasting allows them to predict electricity prices and exploit arbitrage opportunities, thereby maximising profits through strategic import and export of electricity.