Due to human influence and its negative impacts on the world's environment, the world is changing into a cleaner and more sustainable energy system. In both private and public buildings, there is a desire to reduce electricity usage, automate appliances, and optimize the electricity usage of a building. This paper presents the design and implementation of a secured smart home switching system based on wireless communications and self-energy harvesting. The proposed secured smart home switching system integrates access control of the building's electricity, energy harvesting, and storage for the active electronic components and circuitries, and wireless communication for smart switches and sockets. The paper gives two contributions to the design of smart home systems: 1) A practical design and implementation of security (access control system) for a building's power supply which adds a locking feature such that only authorized personnel are capable of altering the power state of the smart sockets and switches in a building, and; 2) A model of energy harvesting and storage system for the active electronic components of the circuitries and wireless communication for smart switches and sockets. The access control involves four stages (a control unit, a comparator unit, a memory unit, and the switching unit). The access control system provides means of access control by having a security keypad that switches ON or OFF the building's electricity, provided the user knows the security pin code (8 coded pins). The proposed system also harvests and stores energy for all the active electronic devices using a photovoltaic system with ultracapacitor energy buffer. The designed secured smart home utilized smart power and switches, and message queuing telemetry transport for ease of controlling energy usage. The experimental results obtained from extensive testing of the prototype shows an improvement in security and energy management in a building.
The growing interest in renewable energy and the falling prices of solar panels place solar electricity in a favourable position for adoption. However, the high-rate adoption of intermittent renewable energy introduces challenges and the potential to create power instability between the available power generation and the load demand. Hence, accurate solar Photovoltaic (PV) power forecasting is essential to maintain system reliability and maximize renewable energy integration. The current solar PV power forecasting approaches are an essential tool to maintain system reliability and maximize renewable energy integration. This paper presents a comprehensive and comparative review of existing Machine Learning (ML) based approaches used in PV power forecasting, focusing on short-term horizons. We provide an overview of factors affecting solar PV power forecasting and an overview of existing PV power forecasting methods in the literature, with a specific focus on ML-based models. To further enhance the comparison and provide more insights into the advancement in the area, we simulate the performance of different ML methods used in solar PV power forecasting and, finally, a discussion on the results of the work.
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