NB-IoT and SCADA Network Enabled Data Acquisition for FFF Model Based Fuel Consumption Forecasting and Energy Security Availability Index Improvement

Accurate forecasting and robust energy security assessment in large-scale supply systems depend strongly on the quality, integrity, and timeliness of input data. In practice, data-quality issues—such as measurement uncertainty, reporting latency, and aggregation errors—can propagate through analytical pipelines and weaken decision-making. While conventional time-series models (e.g., ARIMA) are widely used for energy demand forecasting, they typically assume reliable historical inputs and rarely quantify how the data acquisition infrastructure itself affects predictive accuracy. Recent advances in Internet of Things (IoT) sensing and real-time monitoring, supported by NB-IoT low-power wide-area communications, offer scalable connectivity for geographically distributed energy networks and can improve measurement reliability compared with manual or low-cost reporting frameworks. This study quantitatively evaluates how IoT/NB-IoT–enabled data acquisition influences the performance of a Fourier–Fuzzy–Fibonacci (FFF) fuel consumption forecasting model and the associated Energy Security Availability assessment. We model a traditional manual acquisition scenario characterized by delayed reporting and higher uncertainty, and compare it against a real-time NB-IoT/SCADA-based architecture. The results show that higher-fidelity, low-latency data leads to lower forecasting error and more reliable estimation of strategic storage (safety stock) requirements , enabling improved availability planning. Overall, the findings highlight that next-generation IoT infrastructures are not only enablers of monitoring, but also key drivers of intelligent, resilient, and cost-effective energy management.