The concept of a smart city is aimed at enhancing the quality of life for urban residents, and logistic services are a crucial component of this effort. Despite this, the logistics industry has encountered issues due to the exponential growth of logistics volumes, as well as the complexity of processes and lack of transparency. Consequently, it is necessary to develop an efficient management system that offers traceability and condition monitoring capabilities to ensure the safe and high-quality delivery of goods. Moreover, it is crucial to guarantee the accuracy and dependability of distribution data. In this context, this paper proposes a blockchain-enabled IoT logistics system for the efficient tracking and management of high-price shipments. A smart contract based on blockchain technology has been designed for automatic approval and payment, with the aim of distributing shipping information exclusively among legitimate logistics parties. To ensure authentication, a zero-knowledge proof is used to conceal the blockchain address. Moreover, an intelligent parcel (iParcel) containing piezoresistive sensors is developed to pack delivered goods during the shipping process for violation detection such as severe falls or theft. The iParcels are automatically tracked and traced, and if a violation occurs, the contract is cancelled, and payment is refunded. The transaction fee per party is reasonable, particularly for high-price products that guarantee successful shipment.

Accurate photovoltaic (PV) power forecasting is essential for grid integration, particularly in maritime climates with dynamic weather patterns. This study addresses high-dimensional meteorological data challenges by systematically evaluating 32 variables across four categories (solar irradiance, temperature, atmospheric, hydrometeorological) for day-ahead PV forecasting using long short-term memory (LSTM) networks. Using six years of data from a 350 kWp solar farm in Scotland, we compare satellite-derived data and local weather station measurements. Surprisingly, downward thermal infrared flux—capturing persistent atmospheric moisture and cloud properties in maritime climates—emerged as the most influential predictor despite low correlation (1.93%). When paired with precipitation data, this two-variable combination achieved 99.81% R², outperforming complex multi-variable models. Satellite data consistently surpassed ground measurements, with 9 of the top 10 predictors being satellite derived. Our approach reduces model complexity while improving forecasting accuracy, providing practical solutions for energy systems.

The accelerating global shift toward renewable energy sources is largely attributed to increased investments and the rising demand for electricity, driven by technological progress, population growth, and escalating fuel prices associated with traditional power generation. Despite their benefits, conventional energy systems face challenges such as sensitivity to fluctuations in solar irradiance and temperature, which lead to non-linear electrical behavior and reduced efficiency. In Iraq, for example, the World Bank reports a significant power distribution loss of approximately 51%. To mitigate these inefficiencies, this study introduces a grid-connected photovoltaic (PV) system employing Maximum Power Point Tracking (MPPT) techniques—specifically, the Perturb and Observe (P&O) and Incremental Conductance (I&C) algorithms. These approaches aim to enhance energy extraction from PV arrays under dynamic environmental conditions. System modeling and performance evaluation were conducted using MATLAB/Simulink, focusing on optimizing output and regulating the DC–DC boost converter’s switching frequency. Under varying irradiance (1000–250 W/m²) and temperature (25–50 °C) conditions, I&C algorithm achieved a higher MPPT tracking efficiency of approximately 98.7%, compared to 95.2% for the P&O method. Additionally, I&C demonstrated faster convergence with a response time of 0.15 s and exhibited reduced power ripple (~ 1.2 kW) versus P&O (~ 3.8 kW), confirming its superior dynamic stability and steady-state performance.