Behaviorally-Informed Demand-Side Management in Hyper-Arid Regions: A Study of Nudges in Qatar's Smart Grid

Electricity demand in hyper-arid regions such as Qatar is dominated by cooling loads, which account for nearly 70% of building electricity use. This creates critical challenges for peak demand management, grid stability, and decarbonization. Traditional Demand-Side Management (DSM) strategies focus on technical load-shaping methods but often neglect consumer behavior, limiting their effectiveness. This paper proposes a behaviorally-informed DSM framework that systematically integrates nudges, such as real-time feedback, social comparison, and gamified carbon coin incentives, with advanced forecasting and optimization techniques. Using smart-meter and weather data from pilot sites in Qatar Foundation, we develop hybrid machine learning models (LSTM and XGBoost) for short-term load forecasting, coupled with optimization-based demand shaping and a behavioral utility model. Experimental results demonstrate that nudges can reduce household peak loads by up to 16.8%, improve load factor, and yield daily CO2 savings exceeding 100 kg per household. Moreover, gamified incentives demonstrate superior efficacy in sustaining user engagement over extended periods, significantly outperforming strategies reliant solely on feedback mechanisms or social norms.