Electricity production plays a crucial role in a nation's economic and industrial growth. Being able to predict future electricity demand can help in better planning and resource allocation. In this blog, we dive into forecasting electricity production for the next 12 months using a classic time series model ARIMA.

The Problem We Tackled

The objective of the project was straightforward: Forecast monthly electricity production for the upcoming year based on historical trends. Having a clear understanding of past production patterns helps in estimating future demand and identifying any possible resource bottlenecks well in advance.

Understanding Time Series Data

Time series data is unique because it carries a temporal component — values are dependent on time. Before building any model, it's crucial to understand some key aspects of a time series:

• Trend: The long-term direction the data is moving in.
• Seasonality: Patterns that repeat at regular intervals, like higher electricity usage during summers.
• Stationarity: A stationary series has a constant mean and variance over time, which makes it predictable and easier to model.
Without a stationary series, forecasting models like ARIMA would struggle to make reliable predictions.

Preparing the Data

When we first analyzed the electricity production data, we noticed an upward trend over the years. However, further statistical testing confirmed that the series was not stationary — meaning it needed
transformation before modeling.
To solve this, we used a differencing technique, which involves subtracting the current value from the previous one. This simple transformation helped stabilize the mean of the series and made it
ready for modeling.

Building the ARIMA Model

The ARIMA model — short for AutoRegressive Integrated Moving Average — is a popular method for forecasting time series data. It works by using the dependencies between an observation and a
number of lagged observations (autoregression), the differencing of raw observations (integration), and the dependency between an observation and a residual error from a moving average model.

Choosing the right configuration of ARIMA requires careful analysis of the data’s past behavior, especially looking at autocorrelation patterns. After determining the appropriate parameters, we trained the ARIMA model on our electricity production data.

Forecasting the Future

With the model trained, we forecasted the electricity production for the next 12 months. The forecast showed a continuation of the existing upward trend, highlighting growing energy demands in the near future.

Visualizing the results alongside historical data gave a clear picture of
how well the model captured the underlying patterns and how it
projected them forward.

Final Thoughts

While ARIMA is a classical method, it remains a powerful and interpretable approach for time series forecasting. For future work, it could be beneficial to explore more advanced models such as SARIMA(which captures seasonality more effectively), Facebook's Prophet model, or even deep learning approaches like LSTM networks, especially if the data becomes more complex.

Forecasting electricity production is more than just predicting numbers it's about powering growth, ensuring stability, and planning for a sustainable future.