Load-Frequency Control with Shortterm Load Prediction

Efficient energy management is crucial given 2024's 4.1% worldwide electricity demand increase. This urgency emphasizes the necessity for various, sustainable energy sources in distribution grids. Short-term load prediction approaches using probabilistic power generation and energy storage are crucial for energy usage prediction. Urban energy planners use simulation, probability optimization and modelling to create sustainable energy systems. This study offers a novel hybrid model for smart grids: short-term energy load prediction using transfer learning (TL) and optimized lightGBM (OLGBM). Our two-phase solution tackles Short-term Load Forecasting complexities. First, aberrant supplements and quick deviation selection eliminate missing values and identify key features during data pre-processing. Second, TL-OLGBM learns dynamic time scales and complex data patterns with Bayesian optimization of hyperparameters to improve forecasting accuracy. Additionally, our architecture easily combines the newest Smart and Green Technology, enabling energy system innovation. Comparative performance research shows that our technique outperforms similar models in mean absolute percentage error, accuracy and root mean square error. This hybrid model is a reliable short-term energy load forecast solution that fits the dynamic terrain of smart and green technology integration in modern energy systems.

India’s$27^{\text{t}\text{h}}$Himalayan state Uttarakhand is carved out from northern Uttar Pradesh state on $9^{\text{t}\text{h}}$November 2000. It shares international boundaries with China (Tibet) in the north and Nepal. Neighboring states of Uttarakhand are Himanchal Pradesh in the east and Uttar Pradesh in the south. In the beginning, the state was energy surplus and slowly it has become energy deficit state. Apart from state power regulator’s various directions, short term load prediction is major concern for the state. Power generating company of the state is utilizing its resources and producing maximum power of its capacity and state Transmission company is performing well while Distribution company, which is responsible for power distribution as well as making arrangement of power, is suffering huge loss. Distribution Company is arranging long term power though Power Purchase Agreements. Special attention is needed for power purchase of Short-term power arrangement. It also helps to make electrical infrastructure more efficient. Paper elaborates day ahead load forecast for coming day in association with various affecting factors specially Time. The Artificial Neural Network (ANN)takes historical load and affecting parameters like weather, time, and random disturbances as input and after checking correlation between load and affecting parameters, future load forecast is obtained. Mean Absolute Percent Error (MAPE) is also calculated. With the help of this work, short term electricity arrangement can be improved and help state distribution company to become profitable.

In order to achieve the short-term and high-precision port food transportation port food transportation mooring load prediction, a new principle and method of ship’s port food transportation port food transportation mooring load measurements based on indirect measurement is presented in this study and an algorithm is proposed through which predictions are made by comb the wavelet multi-scale decomposition and reconstruction method. Simulation results show that the combined algorithm has achieved short-term and high-precision port food transportation port food transportation mooring load prediction results, that it has excellent subdivision and self- learning abilities and that it can meet the accuracy requirement of the port food transportation port food transportation mooring load prediction in engineering.

Short-term load prediction is the foundation of dispatch and decision for urgent demand response of power grid. However, traditional load prediction approaches feature not so good adaption due to high random behaviors of individual household loads. In this paper, user home appliance data within extremely short time interval are acquired with help of non-intrusive load identification technique. By in-depth analysis of household home appliance operation characteristics, differentiate parameters and relevant edge values of various states are determined, resulting in state space set of specific home appliance. Time-domain probability distribution function and density function of specific home appliance are derived based on user behavior analysis model construction using continuous-time Markov model. State transition probability matrix and rate matrix are obtained, too. User short-term load prediction procedures are constructed by applying solution of Kolmogorov forward equations. Finally, 100 typical households electricity consumption data of Jiangsu Province in China from July to September are sampled for analysis. The accuracy and effectiveness of proposed method is verified by analysis and comparison results with traditional load prediction method.

Applications such as generator scheduling, household smart device scheduling, transmission line overload management and microgrid islanding autonomy all play key roles in the smart grid ecosystem. Management of these applications could benefit from short-term load prediction, which has been successfully achieved on large-scale systems such as national grids. However, the scale of the data for analysis is much smaller, similar to the load of a single transformer, making prediction difficult. This paper examines several prediction approaches for day and week ahead electrical load of a community of houses that are supplied by a common residential transformer, in particular: artificial neural networks; fuzzy logic; auto-regression; auto-regressive moving average; auto-regressive integrated moving average; and wavelet neural networks. In our evaluation, the methods use pre-recorded electrical load data with added weather information. Data is recorded from a smart-meter trial that took place during 2009-2010 in Ireland, which registered individual household consumption for 17 months. Two different scenarios are investigated, one with 90 houses, and another with 230 houses. Results for the two scenarios are compared and the performances of the evaluated prediction methods are discussed.

In contemporary power networks, short-term load forecasting (STLF) is essential for efficiently managing reserve requirements. During the power-balancing operation, it then helps the grid operator make wise and cost-effective decisions. This paper thoroughly examines STLF techniques including particle swarm optimization (PSO), enhanced particle swarm optimization (EPSO), and artificial neural network (ANN) methods. The benefits and drawbacks of these approaches are shown through in-depth mathematical and graphical analysis as well as a comparative assessment. In order to increase the model's effectiveness for temporal sequence prediction, a hybrid ANN-solar power model is proposed and is evaluated using extensive data from the Xingtai Power Plant in China. The investigation shows improved accuracy and performance in short-term load prediction in terms of root mean square error (RMSE), mean absolute error (MAE), standard deviation (σ), and mean absolute percentage error (MAPE) in 24-h forecasting for the Xingtai Power Plant. Furthermore, the model demonstrates superiority in improving reserve management and balancing supply and demand in a contemporary electrical networks by outperforming earlier models using the Xingtai Power Plant dataset.

According to the data density of the data, select the initial cluster center to fully reflect the distribution characteristics of the data; In the iterative calculation of the new cluster center, the average distance between the data and the center of the class is used as the cluster center of the new iteration to eliminate the influence of the noise point. The cluster evaluation Index Hubert Index and D Index select the optimal cluster number to reflect the spatial distribution of data more accurately. By using the improved k-means algorithm to cluster the data of a variable table, the typical daily load characteristic data of four quarters were obtained, and the short-term load prediction was carried out on this basis. The analysis shows that the load modeling method based on improved k-means is feasible and effective for grid simulation analysis.

A wavelet neural network (WNN) prediction model based on improved wolf pack algorithm(WPA) is proposed to meet the demand of Shanxi's spot market, which requires power sales companies to "quote quantity but not offer price". Customers are classified by considering the differences in electricity consumption characteristics of different industries. The bidirectional comparison method is used to preprocess the original data, which can effectively reduce the errors in the process of data measurement and acquisition. Using WNN to forecast the customer's day-ahead electrical load. Meantime, the intelligent algorithm is used to optimize the network parameters and strengthen the prediction performance of the WNN. The wolf pack algorithm is used to optimize the system, and the walking mode of the wolf pack is improved, which further to improve the optimization ability of the system. An example shows that the model is more accurate in short-term load prediction. Compared with before the improvement, the prediction accuracy has been improved by nearly 2%, and the accuracy has reached 96.4%, which can provide a strong guarantee of the electricity sales company to master the customer load curve, participate in the spot market, and improve its competitiveness.

The global economy has entered a new normal, and the economic environment is evolving at a rapid pace. This requires the establishment of a financial crisis early warning system that can be dynamically analyzed based on historical data information. To address this research objective, this study proposes a k-fold random forest algorithm combined with a time series analysis model as an early warning algorithm for corporate financial crises. The algorithm takes advantage of the ability of the time series analysis model to make short-term forecasts of historical data and uses the time series analysis model to make forecasts of newly constructed financial index data. The k-fold random forest is used to analyze the financial situation of the predicted financial data and achieve the purpose of dynamic financial crisis early warning. The experimental results show that the prediction accuracy of the financial crisis early warning model based on the random forest algorithm and time series is 89%, which indicates that the model is effective and feasible.

The world economy has recently moved in a fresh era, where the financial world is rapidly developing. Various economic crises, such as banking, economic, and currency crises, impose high economic costs, and harm the entire society. This necessitates the creation of an early warning system for financial crisis that can be adaptively analyzed using past information. Early warning systems could prevent the occurrence of business and economic crises by providing a systematic prediction of unfavorable events. Early warning systems are mainly used to detect crises before they do damage and to reduce false alarms of impending crises. Because of the above, this paper studies early warning of the financial crisis of listed companies based on random forest and time series. Besides, it constructs a random forest and Boruta-Random forest (BRF) model with Benford factor to deal with the impact of financial data quality on the financial risk early-warning model. Our model can effectively improve the prediction accuracy of the financial early warning model. The experiments show that, in comparison to RF, BRF can increase the accuracy of financial risk early warning, expand the applicability of RF, as well as provide a fresh perspective for research on listed company financial risk early warning.

Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm

An Identification Technique for Socio-Economic System Models with Time-Varying Parameters

Short-term power load prediction is not only a critical part of power system dispatching but also an essential task for power marketing, grid planning, and other management departments; deep learning is an artificial intelligence method that has gained extensive attention in recent years; this paper selected three models with the most typical signs of deep learning recurrent neural networks and studied their model performance for short-term power load predictions, including power load data preprocessing, feature selection of prediction models, determination of model parameters, and comparison of prediction characteristics between shallow and deep networks, to explore and prove the applicability of deep learning neural networks in short-term power load predictions, ultimately improving the accuracy of short-term load predictions.KeywordsPower loadDeep learningPrediction

An improved self-organizing incremental neural network model for short-term time-series load prediction

Scientific and methodological foundations have been developed for the optimal identification of non-stationary objects transmitted by random time series in control systems of industrial and technological complexes, monitoring of environmental protection and ecology, palynology, medicine based on the use of statistical, dynamic, neural network models and mechanisms for extracting statistical, dynamic specific characteristics of information. The mechanisms for controlling the error in the identification of RTS, based on the use of statistical criteria - the rules of the sequential analysis, Bayesian estimation, ±3σ, confidence interval, have been investigated. The problem of control of the error in the identification of RTS by the confidence interval, the mechanisms of threshold control of the values of the random time series elements, their increments, the error of prediction by statistical, trend, dynamic models, algebraic polynomials, parabolic, cubic interpolation, extrapolation spline functions. Mechanisms for regulating the variables of the random time series identification models based on autoregressive, adaptive smoothing by R. Brown, Newton, Lagrange polynomials, orthogonal algebraic polynomials of 3, 5, 7 orders have been developed. The methodology for assessing the quality of the identification of RTS by mathematical expectation and variance and the criterion of correspondence between the relations of mathematical expectation and variance of two consecutive elements have been implemented. The experimental study was carried out according to the real data of the power grid enterprise. The effectiveness of the implemented mechanisms was studied according to the criteria of the minimum root-mean-square error, labor intensity, cost of information processing.