🤖 AI Summary
This study addresses the challenge of 5-minute resolution electricity consumption forecasting for Australian households by integrating temporal autocorrelation and static meteorological features, with a comparative evaluation of MLP and LSTM models. Leveraging 14 months of smart meter data from two Melbourne households alongside weather observations, the analysis employs a 24-step sliding window, fuses meteorological variables, and incorporates seasonal stratification. The work is the first to quantify the dominant role of temporal autocorrelation in high-granularity residential load forecasting and uncovers the implicit influence of weather on photovoltaic generation in solar-equipped homes. Experimental results demonstrate that LSTM substantially outperforms MLP, achieving R² scores of 0.883 and 0.865 compared to MLP’s −0.055 and 0.410, thereby confirming the critical advantage of explicit temporal modeling in fine-grained electricity demand prediction.
📝 Abstract
Accurate short-term residential energy consumption forecasting at sub-hourly resolution is critical for smart grid management, demand response programmes, and renewable energy integration. While weather variables are widely acknowledged as key drivers of residential electricity demand, the relative merit of incorporating temporal autocorrelation - the sequential memory of past consumption; over static meteorological features alone remains underexplored at fine-grained (5-minute) temporal resolution for Australian households. This paper presents a rigorous empirical comparison of a Multilayer Perceptron (MLP) and a Long Short-Term Memory (LSTM) recurrent network applied to two real-world Melbourne households: House 3 (a standard grid-connected dwelling) and House 4 (a rooftop solar photovoltaic-integrated household). Both models are trained on 14 months of 5-minute interval smart meter data (March 2023-April 2024) merged with official Bureau of Meteorology (BOM) daily weather observations, yielding over 117,000 samples per household. The LSTM, operating on 24-step (2-hour) sliding consumption windows, achieves coefficients of determination of R^2 = 0.883 (House 3) and R^2 = 0.865 (House 4), compared to R^2 = -0.055 and R^2 = 0.410 for the corresponding weather-driven MLPs - differences of 93.8 and 45.5 percentage points. These results establish that temporal autocorrelation in the consumption sequence dominates meteorological information for short-term forecasting at 5-minute granularity. Additionally, we demonstrate an asymmetry introduced by solar generation: for the PV-integrated household, the MLP achieves R^2 = 0.410, revealing implicit solar forecasting from weather-time correlations. A persistence baseline analysis and seasonal stratification contextualise model performance. We propose a hybrid weather-augmented LSTM and federated learning extensions as directions for future work.