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Expect: EXplainable Prediction Model for Energy ConsumpTion

Amira Mouakher, Wissem Inoubli, Chahinez Ounoughi, Andrea Kő

2022Mathematics21 citationsDOIOpen Access PDF

Abstract

With the steady growth of energy demands and resource depletion in today’s world, energy prediction models have gained more and more attention recently. Reducing energy consumption and carbon footprint are critical factors for achieving efficiency in sustainable cities. Unfortunately, traditional energy prediction models focus only on prediction performance. However, explainable models are essential to building trust and engaging users to accept AI-based systems. In this paper, we propose an explainable deep learning model, called Expect, to forecast energy consumption from time series effectively. Our results demonstrate our proposal’s robustness and accuracy when compared to the baseline methods.

Topics & Concepts

Robustness (evolution)Computer scienceEnergy consumptionCarbon footprintEfficient energy useConsumption (sociology)Predictive modellingEnergy (signal processing)Baseline (sea)Artificial intelligenceResource consumptionEnvironmental economicsMachine learningEngineeringEconomicsStatisticsGreenhouse gasSocial scienceGeologyBiochemistryEcologySociologyBiologyElectrical engineeringChemistryGeneOceanographyMathematicsEnergy Load and Power ForecastingTraffic Prediction and Management TechniquesStock Market Forecasting Methods
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