2 research outputs found
Short-term forecasting of load and renewable energy using artifical neural network
Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for short-term electrical load forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m2) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularisation (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand. © 2021 Seventh Sense Research Group
Intelligent Energy Management with IoT Framework in Smart Cities Using Intelligent Analysis: An Application of Machine Learning Methods for Complex Networks and Systems
Smart buildings are increasingly using Internet of Things (IoT)-based
wireless sensing systems to reduce their energy consumption and environmental
impact. As a result of their compact size and ability to sense, measure, and
compute all electrical properties, Internet of Things devices have become
increasingly important in our society. A major contribution of this study is
the development of a comprehensive IoT-based framework for smart city energy
management, incorporating multiple components of IoT architecture and
framework. An IoT framework for intelligent energy management applications that
employ intelligent analysis is an essential system component that collects and
stores information. Additionally, it serves as a platform for the development
of applications by other companies. Furthermore, we have studied intelligent
energy management solutions based on intelligent mechanisms. The depletion of
energy resources and the increase in energy demand have led to an increase in
energy consumption and building maintenance. The data collected is used to
monitor, control, and enhance the efficiency of the system