PREDICTIVE ANALYSIS OF ENERGY CONSUMPTION PATTERNS USING MACHINE LEARNING TECHNIQUES

Abstract

Accurate forecasting of power consumption is crucial for coping with resources and promoting sustainability in contemporary societies. This paper examines using Long Short-Term Memory (LSTM) networks, integrated with Monte Carlo Dropout, to improve the precision and uncertainty quantification of electricity intake predictions. By using a complete time series dataset of hourly power consumption, our model done a root imply rectangular mistakes (RMSE) of 5005. Ninety three on the take a look at set, outperforming traditional fashions. Incorporating function engineering strategies, the model successfully identifies seasonal traits and styles, strengthening its predictive abilities. Monte Carlo Dropout was carried out to seize the uncertainty inherent in electricity consumption forecasts, supplying more than one prediction samples and self-assurance intervals. Results indicated an average RMSE of 16015.Sixty eight throughout move-validation folds, with confidence durations offering perception into forecast reliability. This look at underscores the value of LSTM networks in time series forecasting and highlights the significance of uncertainty quantification in energy intake predictions. The findings make a contribution to optimizing electricity control practices and aid decision-making in each the electricity sector and related social technological know-how fields.