 AI-Driven Market Demand Forecasting for IoT Hardware in Smart Buildings: Implications for Investment in the Digital EconomyZhiyan Jiang GBP Proceedings Series, 2025, vol. 14, 163-169 Abstract: Driven by the growth of the digital economy, Internet of Things (IoT) hardware for smart buildings has increasingly become a critical foundation for smart cities and sustainable architecture. Accurately forecasting market demand has emerged as a key challenge for investment decision-making and enterprise strategy development. This study, based on research in the AI + hardware domain, examines specific products such as mobile phones, intelligent sensing devices, and building control equipment, exploring the role of artificial intelligence in market prediction. A comprehensive prediction framework is established using time series models and machine learning algorithms, integrating factors such as product types, application scenarios, and regional markets into the modeling process. Leveraging historical data and external environmental indicators, an AI-driven prediction method is proposed, and its implications for investment in digital economy sectors and enterprise product iteration are analyzed. The study aims to provide enterprises with more precise and forward-looking guidance for strategic planning and market positioning. Keywords: artificial intelligence; intelligent building; Internet of Things; hardware; market demand forecast; digital economy; investment (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:axf:gbppsa:v:14:y:2025:i::p:163-169 Access Statistics for this article More articles in GBP Proceedings Series from Scientific Open Access Publishing |
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