Load Forecasting Based on Weighted Grey Relational Degree and Improved ABC-SVM

This research paper investigates the application of artificial neural networks (ANNs) and fuzzy logic methods for short-term load forecasting in the Western Libyan Electric Network. Historical hourly load data collected during the year 2023 by the General Electricity Company of Libya (GECOL). The study aims to develop accurate and reliable load forecasting models to support efficient grid operation and resource planning. Historical load data and meteorological variables are utilized to train and evaluate the forecasting models. The performance of the ANNs and fuzzy logic methods is compared, and the suitability of each approach for load forecasting in the Western Libyan Electric Network is assessed. The results demonstrate the effectiveness of both ANNs and fuzzy logic methods in short-term load forecasting, providing valuable insights for electricity providers and policymakers in the region. Keywords: neural' networks, fuzzy logic, load forecasting

Load forecasting is of great significance for the early planning of integrated energy system. However, for new buildings and old buildings without historical data, there is no historical load data, and the traditional load forecasting model is no longer applicable. Therefore, for this kind of buildings, based on load correlation analysis, this paper proposes a long-term load forecasting model based on feature fusion and LightGBM. After effective feature analysis of different types of features, this paper uses the method of feature fusion to construct the final input, and with the learning advantage of LightGBM model for regression problem. It uses histogram algorithm to discretize the data, speeds up the calculation, and uses gradient unilateral sampling algorithm to reduce the number of training samples in iteration and avoid over-fitting. We execute an experimental analysis on the load simulation dataset of 205 typical buildings, and finally evaluate the accuracy of the model through the parameters such as statistical metrics MSE, MAE and correlations coefficient. The experiment results show that compared with other algorithms, the proposed method has higher accuracy and faster speed, which is of great significance for the load forecasting of new and old buildings without historical data.

The evolution of conventional power systems into smart grids demands intelligent and efficient energy management solutions. Accurate load forecasting is essential for optimal generation scheduling, demand-side management, and system reliability. However, traditional methods often fail to capture the nonlinear and time-varying nature of electrical loads. This work proposes a machine learning–based approach for local load forecasting and grid stability enhancement. Techniques such as Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are utilized to analyze historical load and weather data for improved prediction accuracy. In addition, a predictive control strategy is implemented to maintain system stability by mitigating voltage fluctuations, frequency deviations, and load imbalances. The proposed model dynamically adjusts control actions such as demand response and load shedding. Simulation results demonstrate reduced forecasting errors (MAE, RMSE) and improved voltage and frequency regulation, ensuring enhanced reliability, efficiency, and stable operation of modern smart grid systems. Keywords: Smart Grid; Machine Learning; Load Forecasting; Grid Stability; Predictive Control.

An accurate medium term load forecasting is significant for power generation scheduling, economic and reliable operation in power system. Most of classical approach for medium term load forecasting only consider total daily load demand. This approach may not provide accurate results since the load demand is fluctuated in a day. In this paper, a hybrid Ant-Lion Optimizer Least-square Support Vector Machine (ALO-LSSVM) is proposed to forecast 24-hour load demand for the next year. Ant-Lion Optimizer (ALO) is utilized to optimize the RBF Kernel parameters in Least-Square Support Vector Machine (LS-SVM). The objective of the optimization is to minimize the Mean Absolute Percentage Error (MAPE). The performance of ALO-LSSVM technique was compared with those obtained from LS-SVM technique through a 10-fold cross-validation procedure. The historical hourly load data are analyzed and appropriate features are selected for the model. There are 24 inputs and 24 outputs vectors for this model which represents 24-hour load demand for whole year. The results revealed that the high accuracy of prediction could be achieved using ALO-LSSVM.

In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.

The accurate prediction of power system load is extremely important for the operation of the power market and the safe operation of the power grid. In order to improve the accuracy of short-term load forecasting of power systems, a combination model based on long short term memory network (LSTM) and light gradient boosting machine (LightGBM) is proposed. The experiment first decomposes historical load data by EMD, uses historical weather data and load data decomposed by EMD to establish LSTM prediction model and LightGBM prediction model respectively, and then these two predicted values are linearly combined to obtain the final predicted value. The electrical load data of the 2016 Electrician Mathematical Contest in Modeling is used as an example to verify. The experimental results show that the LSTM-LightGBM combined model has higher forecasting accuracy and application prospects for power load forecasting than traditional load forecasting methods and standard LSTM and LightGBM load forecasting methods.

An adaptive chaotic artificial bee colony algorithm for short-term hydrothermal generation scheduling

Identification-based controller design using cloud model for course-keeping of ships in waves

The success of any Intrusion Detection Systems (IDSs) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features is very important topic for an intrusion detection problem. Support vector machine (SVM) has been employed to provide potential solutions for the IDSs problem. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Artificial Bee Colony algorithm (ABC) is an optimization method, which is not only has strong global search capability, but also is very easy to implement. Thus, the proposed ABC-SVM model is applied to determine free parameters of SVM and feature selection at building intrusion detection system. The standard ABC is used to determine free parameters of support vector machine and the binary ABC is to obtain the optimum feature selection for IDSs from KDD Cup 99 data set. The experimental results indicate that the ABC-SVM method can achieve higher accuracy rate than Particle swarm optimization (PSO) and GA-SVM algorithms in the same time.

A new hybrid technique using support vector machines (SVM) to forecast the next `24' hours load is proposed in this paper. Four modules consisting of the basic SVM, peak and valley SVM, averager and forecaster and adaptive combiner form the integrated method for load forecasting. The proposed architecture can forecast the next `24' hours load. The basic SVM uses the historical data of load and temperature to predict the next `24' hour's load, while the peak and valley SVM uses the past peak and valley data of load and temperatures respectively. The averager captures the average variation of the load from the previous load behavior, while the adaptive combiner uses the weighted combination of outputs from the basic SVM and the forecaster, to forecast the final load. The statistical and artificial intelligence based methods are conceptually incorporated into the architecture to exploit the advantages and disadvantages of each technique.

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

In order to avoid the adverse effects of load transfer, power outage and small power supply on bus load forecasting in bus power supply area, a short-term load forecasting algorithm for substation bus based on multi-source data characteristics is proposed. By converting the load of the bus to the ideal power load in the power supply area of the bus, the ideal power load is corrected as the historical load data, and the algorithm of multi-source data characteristic load forecasting is used to obtain the preliminary forecasting results. At the same time, the values of various influencing factors on the day to be forecasted are obtained. The forecasting results eliminate various influencing factors and indirectly predict the load value of the bus. Based on this, the experiment proves that the application of short-term load forecasting algorithm of substation bus based on multi-source data characteristics can significantly improve the accuracy of bus load forecasting with small power supply in the power supply area, compared with the direct forecasting method which takes the load value of bus network as historical data.

A short-term load forecasting model based on mixup and transfer learning

Since load forecasting plays an important role in the planning and operation of power industry, substantial efforts are made in improving the accuracy and reliability of load forecasting.In this paper, we develop a novel hybrid approach based on phase space reconstruction and least square support vector for the short-term load forecasting.However, the proper parameters in phase space reconstruction and least square vector machine have a significant effect on the forecasting performance, and there is no standard solution for the parameter estimation problem.Therefore, in this paper, the genetic algorithm (GA) approach is employed to optimize the parameters of both phase space reconstruction and least square support vector machine together.The experimental results suggest that the joint optimization parameter is superior to the separate optimization solutions.

PurposeThe purpose of this paper is to use an ABC algorithm to improve the thrust–torque ratio of a rotating-wing unmanned aerial vehicle (UAV) model.Design/methodology/approachThe design of UAVs, such as aircraft, drones, helicopters, has become one of the popular engineering areas with the development of technology. This study aims to improve the value of thrust–torque ratio of an unmanned helicopter. For this purpose, an unmanned helicopter was built at the Faculty of Aeronautics and Astronautics, Erciyes University. The maximum thrust–torque ratio was calculated considering the blade length, blade chord width, blade mass density and blade twist angle. For calculation, artificial bee colony (ABC) algorithm was used. By using ABC algorithm, the maximum thrust–torque ratio was obtained against the optimum input values. For this purpose, a model with four inputs and a single output is formed. In the generated system model, optimum thrust–torque ratio was calculated by changing the input values used in the ±5% range. As a result of this study, approximately 31% improvement was achieved. According to these results, the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.FindingsAccording to these results, approximately 31% improvement was achieved, and the proposed approach will provide convenience to the designers in the design of the rotating-wing UAV.Research limitations/implicationsIt takes a long time to obtain the optimum thrust–torque ratio value through the ABC algorithm method.Practical implicationsUsing ABC algorithm provides to improve the value of thrust–torque ratio of an unmanned helicopter. With this algorithm, unmanned helicopter flies more than ever. Thus, the presented method based on the ABC algorithm is more efficient.Social implicationsThe application of the ABC algorithm method can be used effectively to calculate the thrust–torque ratio in UAV.Originality/valueProviding an original and penetrating a method that saves time and reduces the cost to improve the value of thrust–torque ratio of an unmanned helicopter.