Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities

Abstract

Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.

Hybridised Artificial Neural Network Model with Slime Mould Algorithm: A Novel Methodology for Prediction of Urban Stochastic Water Demand

“…The main advantages of optimisation algorithms are their ability to select the optimal values of parameters of the system under different conditions, and they have time-saving qualities. Recently, a multi-verse optimiser (MVO) proposed by Mirjalili et al [23] to solve various optimisation problems, for example, has been used for energy management in smart cities [24] and multi-level image segmentation [25]. Additionally, a backtracking search algorithm (BSA) has been utilised to tackle several optimisation issues, such as predicting urban water demand depending on previous water consumption data [26], photovoltaic models [27] and power signals [28].…”

Optimization Approaches for Demand-Side Management in the Smart Grid: A Systematic Mapping Study

“…Math-based heuristics are only based on mathematical equations and do not obtain their inspiration from a natural phenomenon. Few instances have been found for the peak/energy cost optimization problem: sine-cosine [42,65] or multi-objective arithmetic optimization.…”

“…On all these types of data, the great frequency of use of genetic algorithms and particle swarm optimization shows their good exploration/exploitation capabilities of the solution space. Many comparative studies [65,69,70] rank them amongst top algorithms for optimizing PAR and cost. Their rank (relative to each other) varies from one study to another, depending on the multi-objective optimization function (e.g., cost, PAR, waiting time) and also according to the conditions and input parameters (e.g., number of users, integration of renewable or storage systems).…”

Exploiting IoT and Its Enabled Technologies for Irrigation Needs in Agriculture

“…Neural networks embed the process of imitating the operations of the human brain to perform tasks through a non-explicit programming structure, where sample data or training data are used to fetch insights from the available data resources. Machine learning is considered the subset of neural networks [50][51][52][53][54][55][56][57][58][59][60][61]. With the enormous amount of data, machine learning or artificial neural networks would help in identifying the pattern hidden inside the data.…”