1. IoT-based off-grid solar power supply : design, implementation, and case study of energy consumption control using forecasted solar irradiationMarijan Španer, Mitja Truntič, Darko Hercog, 2025, original scientific article Abstract: This article presents the development and implementation of an IoT-enabled, off-grid solar power supply prototype designed to power a range of electrical devices. The developed system comprises a Photovoltaic panel, a Maximum Power Point Tracking (MPPT) charger, a 2.5 kWh/24 V high-performance LiFePO4 battery bank with a Battery Management System, an embedded controller with IoT connectivity, and DC/DC and DC/AC converters. The PV panel serves as the primary energy source, with the MPPT controller optimizing battery charging, while the DC/DC and DC/AC converters supply power to the connected electrical devices. The article includes a case study of a developed platform for powering an information and advertising system. The system features a predictive energy management algorithm, which optimizes the appliance operation based on daily solar irradiance forecasts and real-time battery State-of-Charge monitoring. The IoT-enabled controller obtains solar irradiance forecasts from an online meteorological service via API calls and uses these data to estimate energy availability for the next day. Using this prediction, the system schedules and prioritizes the operations of connected electrical devices dynamically to optimize the performance and prevent critical battery discharge. The IoT-based controller is equipped with both Wi-Fi and an LTE modem, enabling communication with online services via wireless or cellular networks.
Keywords: energy consumption control, forecasted solar irradiation, power management, off-grid power supply, photovoltaic, solar, IoT, LTE, Wi-Fi, ESP32
Published in DKUM: 14.11.2025; Views: 0; Downloads: 6
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2. Methods and models for electric load forecasting : a comprehensive reviewMahmoud A. Hammad, Borut Jereb, Bojan Rosi, Dejan Dragan, 2020, original scientific article Abstract: Electric load forecasting (ELF) is a vital process in the planning of the electricity industry and plays a crucial role in electric capacity scheduling and power systems management and, therefore, it has attracted increasing academic interest. Hence, the accuracy of electric load forecasting has great importance for energy generating capacity scheduling and power system management. This paper presents a review of forecasting methods and models for electricity load. About 45 academic papers have been used for the comparison based on specified criteria such as time frame, inputs, outputs, the scale of the project, and value. The review reveals that despite the relative simplicity of all reviewed models, the regression analysis is still widely used and efficient for long-term forecasting. As for short-term predictions, machine learning or artificial intelligence-based models such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Fuzzy logic are favored.
Keywords: methods, models, electric load forecasting, modeling electricity loads, electricity industry, power management, logistics
Published in DKUM: 22.08.2024; Views: 95; Downloads: 11
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3. A Machine Learning Application for the Energy Flexibility Assessment of a Distribution Network for ConsumersJaka Rober, Leon Maruša, Miloš Beković, 2023, original scientific article Keywords: flexibility, baseline, demand response, distribution transformer, congestion management, power flow control, peak shaving, load shifting, predictive models, machine learning
Published in DKUM: 05.01.2024; Views: 386; Downloads: 62
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4. Renaissance management : the rebirth of energy and innovation in people and organizationsStephen Carter, 1999, manual Keywords: management, renaissance, reengineering, changes, individualism, society, human potential, human resource management, work, employment, creativity, motivation, working power, energetic system, organizational development, working organization, flexibility, team work, manager, partnership
Published in DKUM: 01.06.2012; Views: 2106; Downloads: 89
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