This paper applies Chaos Theory to analyze time series data of electricity demand from the Iberian Electricity Market. After rejecting the hypothesis of independent and identically distributed (i.i.d.) data using the BDS test, the chaotic structure of the series is assessed through key system invariants, including the maximal Lyapunov exponent, correlation dimension, and Kolmogorov-Sinai entropy. These invariants are derived from the reconstructed attractor using appropriate delay time and embedding dimension. The results indicate chaotic behavior in the hourly electricity demand. By leveraging the reconstructed attractor and averaging the projections of neighboring points into the future, an accurate forecast is achieved. The approach has a good performance and demonstrates the potential of nonlinear techniques for short-term prediction.