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Electrical Energy is an essential commodity which significantly contributes to the economic development of any country. Many non-linear factors contribute to the final output of electrical energy demand. In order to efficiently predict electrical energy demand, many time-series analysis and multivariate techniques have been suggested. In order for these methods to accurately work, an enormous quantity of historical dataset is essential which sometimes are not available, inadequate and inaccurate. To overcome some of these challenges, this paper presents an Artificial Neural Network based method for Electrical Energy Demand Forecasting using a case study of Lagos state, Nigeria. The predicted values are compared with actual values to estimate the performance of the proposed technique.

In Nigeria, power is one of the major problems and this deters the reliability and effectiveness of a video surveillance system as the case may be. Also, as criminal organizations tend to destroy every physical storage after an operation, providing a suitable backup to the cloud is a viable solution to serve as a failsafe plan in case of hardware eventualities. This work focuses on the design and construction of a real-time power and data backup surveillance system for security of lives and properties. Construction of a sufficient power bank backup system was developed in real time with average power outage duration in Nigeria considered. Lithium ion batteries with a cascade connection was used to provide alternate power to the system in a situation of power outage. An Arduino microcontroller controlling relay modules was used to ensure optimum battery life and efficiency and an IP camera was used for surveillance as well as cloud storage. This research was implemented and evaluated to measure the efficiency of the system. The results show that the entire system has the capacity to switch between the direct power source and the alternative power source, it could last for 100- 156hours after full charge in absence of power supply. The data backup was stored to a secured cloud and could only be accessed by authorized users when such is required.

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Retrofitting technologies have helped to manage energy consumptions in residential, public and industrial buildings. However, understanding of the technical and economic considerations for selection of appropriate retrofitting technology is still evolving and divergent. Thus, this study presents a framework that combines techno-economic requirements as a means for evaluating the important retrofitting criteria and suitable lighting retrofit technologies for building projects. The framework is hinged on the unique features of entropy fuzzy and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods. The analysis of the lighting technology selection was performed from technical, economic and techno-economic perspectives. During the application of the proposed framework, four lighting technologies (CFL, T5, E-ballast and T8-electronic) and nine techno-economic criteria were considered. The most and least important techno-economic criteria for the case study were net present value and electricity saved, respectively. The least and most suitable retrofitting technologies were T8-electronic and CFL, respectively, from techno-economic perspective. T5 and T8-electronic were identified as the most suitable lighting technologies from an economic and technical perspectives, respectively. This discrepancy in the results justified the need for the techno-economic approach for the retrofitting technologies evaluation.