Energy Consumption

This study aimed to design and implement a low-cost microcontroller-based system for monitoring the energy consumption of individual workshop machines, addressing the limitations of conventional centralized metering systems that fail to provide machine- specific data. The literature review examined previous work on energy monitoring technologies, including commercial, open-source, and academic systems, highlighting the growing role of the Internet of Things (IoT) in enabling real-time data acquisition and remote monitoring. It emphasized the need for affordable, scalable, and educationally adaptable solutions for developing regions, where technical expertise and financial resources are limited. The research adopted an experimental design methodology involving hardware and software integration. The system was built using Arduino Nano and ESP32 microcontrollers, ZMPT101B voltage and SCT-013 current sensors, an LCD display, and a ThingSpeak IoT cloud interface. Mathematical modeling was applied to compute voltage, current, power, energy, and cost, while SolidWorks was used for casing design. Calibration and testing were conducted under varying load conditions to assess accuracy, response time, and data stability. Data were logged both locally on an SD card and remotely on the cloud for redundancy and analysis. Results indicated that the system achieved high accuracy within ±1% for voltage and ±5% for current, with an overall efficiency of 95% and IoT data transfer uptime of 98%. The developed prototype successfully provided real-time monitoring, stable performance, and reliable data transmission. The study concluded that the Arduino-based energy monitoring system is a cost-effective, scalable, and efficient solution suitable for educational, domestic,

Energy consumption facilitates economic growth but it is a major source of carbon emission, leading to the dilemma in policy priority between economic growth and pollution reduction. Therefore, this study empirically examined the relationship between energy consumption, carbon emissions and economic growth in Nigeria using cointegration and dynamic causality analysis, with annual time series data for the period 1981 to 2021. A good number of econometric techniques were conducted, which include; descriptive statistics, correlation coefficient, unit root test, granger causality test, optimal lag selection criteria test and co-integration test using Autoregressive Distribution Lag (ARDL) Bound Test and ARDL model Approach. Granger long- run dynamic analysis were conducted using error correction model (ECM) framework to explore the causal relationships between the variables. The study revealed evidence of relationship between energy use, electricity consumption, CO2 emission and economic growth in Nigeria. A positive but insignificant relationship exist between energy use and economic growth, electricity consumption and economic growth, while a negative and insignificant relationship between CO2 emission and economic growth in the long-run during the study period. During the lagged period, CO2 emission and economic growth showed positive and significant relationship in the long-run. The study also revealed that a unidirectional causality exists from economic growth to energy use, electricity consumption to economic growth in the long run, while a bidirectional long-run causality exists between CO2 emission and economic growth. An important policy implication is that energy consumption has positive influence on economic growth in Nigeria, thus as higher energy consumption also means higher pollution in the long-run, policymakers should diversify and explore alternative energy sources for meeting up the increasing energy demand and reducing the effect of carbon on her citizens.