Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Abstract

Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong nonlinearity, multiple factors, and large fluctuation, which lead to it being difficult to receive more accurate forecasting performances. To solve the abovementioned characteristics of a MEL time series, the least squares support vector machine (LS-SVR) hybridizing with meta-heuristic algorithms is applied to simulate the nonlinear system of a MEL time series. As it is known that the fruit fly optimization algorithm (FOA) has several embedded drawbacks that lead to problems, this paper applies a quantum computing mechanism (QCM) to empower each fruit fly to possess quantum behavior during the searching processes, i.e., a QFOA algorithm. Eventually, the cat chaotic mapping function is introduced into the QFOA algorithm, namely CQFOA, to implement the chaotic global perturbation strategy to help fruit flies to escape from the local optima while the population’s diversity is poor. Finally, a new MEL forecasting method, namely the LS-SVR-CQFOA model, is established by hybridizing the LS-SVR model with CQFOA. The experimental results illustrate that, in three datasets, the proposed LS-SVR-CQFOA model is superior to other alternative models, including BPNN (back-propagation neural networks), LS-SVR-CQPSO (LS-SVR with chaotic quantum particle swarm optimization algorithm), LS-SVR-CQTS (LS-SVR with chaotic quantum tabu search algorithm), LS-SVR-CQGA (LS-SVR with chaotic quantum genetic algorithm), LS-SVR-CQBA (LS-SVR with chaotic quantum bat algorithm), LS-SVR-FOA, and LS-SVR-QFOA models, in terms of forecasting accuracy indexes. In addition, it passes the significance test at a 97.5% confidence level.

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

“…Artificial intelligence methods are ANN, Particle Swarm Optimization (PSO), etc. Classifier-based approaches are widely used for forecasting, such as SWA (Sperm Whale Algorithm) + LSSVM (Least Square Support Vector Machine) [13], SVM + PSO [14][15][16], empirical mode decomposition + Support Vector Regressor (SVR) [17], FWPT (Flexible Wavelet Packet Transform), TVABC (Time-Varying Artificial Bee Colony), LSSVM (FWPT + LSSVM + TVABC) [18], LSSVR + fruit fly algorithm [19], phase space reconstruction + bi-square kernel regression [20] and DE (Differential Evaluation) + SVM [21]. Although the aforementioned methods show reasonable results in load or price forecasting, they are computationally complex.…”

“…Therefore, MAE and NRMSE are suitable performance measures. The formulas of MAE and NRMSE are given in Equations (18) and (19), respectively.…”

Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting

“…Classical deterministic theories are mainly applied to conduct the traditional short-term load forecasting. Such as time series method [3], back-propagation neural network (BPNN) model [4], gray model [5,6], and support vector regression [7][8][9], etc. Although these methods are widely adopted, there are still some outstanding problems, for example, (1) it is difficult to simulate the relationships between the variables affecting the electricity loads and the loads themselves by accurate mathematical model; (2) the forecasting accuracy requires improvements; (3) the forecasting effect is not satisfied; and (4) the real situation of the electricity load cannot be reflected in real time.…”

Construction of EMD-SVR-QGA Model for Electricity Consumption: Case of University Dormitory

“…However, the worst shortcoming is that its population diversity would decrease along with repeated iterative computation and leads to some problems such as time-consuming, slow convergence, and trapping into local optima. Recently, the quantum computing mechanism [50,56,57] has been applied to be hybridized with the genetic algorithm [50]. The principal quantum computing mechanism, such as qubit, quantum superposition, and quantum entanglement are used to represent the chromosome of QGA into the qubit coding; eventually, the quantum rotation gate is also employed to renew the chromosomes in the modeling process.…”