Forecasting electricity consumption using the Principal Component Analysis method

Introduction. Reducing the energy intensity of production is one of the directions of development of enterprises, regions, states, as it affects the level of economic development and environmental well-being. One of the methods that contribute to the rationalization of the use of electric energy is forecasting. The purpose of the study is to develop a model for forecasting electricity consumption, which allows obtaining a reliable forecast of electricity consumption. To achieve this goal, an analysis of literary sources reflecting the current trends of predictive analytics in solving the problem of forecasting electricity consumption was carried out. Materials and methods of research. Singular spectral analysis and deep learning algorithms (multilayer perceptron) were used in the work. At the initial stage of the study, a model of a fully connected neural network (multilayer perceptron) was built, the prediction accuracy of which was more than 94%. Then the algorithm of singular spectral analysis was implemented, and the initial time series of power consumption was decomposed into additive components. The resulting decompositions were used to create a hybrid forecasting model. Research results. A number of experimental studies have been conducted that meet the goal of developing models that most accurately approximate experimental data. The result of the study are two forecasting models: an artificial neural network model and a hybrid model that includes a singular spectral analysis to decompose a number of power consumption into trend and harmonic components and an artificial neural network to predict them. Discussion. With the help of the hybrid model, an accurate result was obtained – the average absolute percentage error for the year is 4.04%. Forecasting was carried out for a year (by months) using data from the previous two years as a training subset. The neural network model provides less accuracy in predicting power consumption over 12 months compared to the hybrid model. Conclusion. Thus, the use of singular spectral analysis made it possible to reduce the magnitude of the forecast error by almost 1%, which confirms the effectiveness of the method and the relevance of the development of hybrid forecasting models. Resume. As a result of the conducted research, it was revealed that one of the most popular and effective forecasting methods is singular spectral analysis and the use of various deep learning algorithms. The developed hybrid model of electric energy consumption for a mining and metallurgical enterprise allows you to build a forecast of electricity consumption, the accuracy of which is more than 95%. Suggestions for practical application and directions for future research. The research results can be useful in the process of production management, since an accurate forecast of electricity consumption is important when planning production, scheduling equipment repairs and maintenance of the electrical network. The prospect of further research is to increase the forecasting horizon, the volume of the initial sample and taking into account additional factors characterizing the technological process.

The forecast of electricity consumption is a prediction of the use of electricity for the future by referring to the use of electricity in past data. Estimates of electricity consumption needs are intended to estimate how much electricity consumption is used by customers and must be provided by PT PLN (PERSERO) ULP SIBUHUAN as a provider of electrical energy services. Population growth occurs along with the development of an area, so it affects the demand for electricity and the need for electricity consumption. Load mapping must be done to maintain the continuity and distribution of electrical energy to customers. One way to preserve the continuity of service is to estimate electric power consumption using several forecasting methods. One of the methods that can be used is to combine several load forecasting methods called the combined method. The combined method is a model that incorporates various estimation methods, including econometrics, analytics, and trends, using historical customer data originating from BPS (Central Statistics Agency) Padang Lawas Regency and PT. PLN (PERSERO) ULP SIBUHUAN. The estimated load between 2021 and 2025 is 625,070,452 kWh, with an increase in electricity consumption of 20.7%, household expenses increasing by 1.8%, and business sector expenses by 1.35%. The method used is to calculate manually using Microsoft Excel to obtain forecasting values for population growth; increasing electricity consumption is very important for planning electric power generation, development, and planning of electric power distribution and mapping of loads on customers.

The large airport terminal is the city's transportation hub and logistics center, and its building electricity consumption is twice or even higher than that of general large public buildings. Energy-saving and consumption reduction of the terminal building is not only the need to improve the operational efficiency and reduce the operation cost of the airport but also the need to realize the goal of "double carbon" in China. In this study, a large airport terminal in Beijing is taken as the research object. According to the characteristics of its main electrical equipment system, and the measured data of passenger flow from January 2020 to February 2023, combined with Pearson correlation analysis, K-means cluster analysis and multiple regression analysis, the influence and correlation characteristics of building scale, passenger flow and outdoor meteorological parameters on its electricity consumption are analyzed. The results show that: 1) The daily electricity consumption of the terminal can be analyzed by five levels according to the daily passenger flow, namely, 10,000 < N ≤ 30,000,30,000 < N ≤ 50,000, 50,000 < N ≤ 70,000, 70,000 < N ≤ 100,000, and 100,000 < N ≤ 130,000; 2) The electricity consumption of the terminal can be divided into three categories: basic electricity consumption related to the building scale of the terminal, variable electricity consumption I related to passenger flow, and variable electricity consumption II related to outdoor air temperature and passenger flow. Based on this, the terminal's daily electricity consumption prediction model is constructed, and the measured data verify the model's effectiveness. 3) Put forward the evaluation reference value of the daily electricity consumption of different passenger flow in this terminal, When the year passenger flow reaches the design value of 45 million, the normal daily electricity consumption level ranges from 0.44 to 0.48 kW·h/(m2·day). The research results can provide methods and evaluation reference for electricity consumption prediction, and accurate control of electricity consumption of main equipment systems in airport terminal.

Relevance. The need to enhance the precision of electricity consumption forecasting for improving energy efficiency and, consequently, enhancing the competitiveness of manufactured products by reducing the proportion of electricity costs in their total cost. When determining forecast indicators of electricity consumption by industrial enterprises, it is important to apply contemporary high-precision forecasting methods. Only 20–30 forecasting methods of the 150 existing ones are actively implemented in practice. An examination of prevailing forecasting methodologies used by industrial enterprises reveals that they are mainly based either on expert assessments of electricity volumes or on accounting for specific electricity consumption (per unit of product manufactured). Aim. To elevate the accuracy of electricity consumption forecasting at industrial enterprises by using artificial intelligence methods, specifically, artificial neural network techniques, including the Long-Short Term Memory (LSTM) approach. Methods. When developing the forecasting model, artificial neural network techniques were adopted, with a particular emphasis on the Long-Short Term Memory (LSTM) method. For primary data processing, Gaussian distribution principles and normalization/scaling techniques were applied. Results. Substantiated computationally by applying the proposed model based on the artificial neural network technique for forecasting electricity consumption of industrial enterprises. A significant advantage of this method is its capability for learning and adaptability to forecasting. Real-time computations demonstrate its successful implementation, attributed primarily to appropriate selection of input layers and mitigation of random variables.

The forecasting technology of electricity consumption is essential both in power system scheduling and in the open power market. However, in the current electricity sales company's electricity consumption forecasting problems, the historical electricity data and the error distribution of electricity consumption forecasting are usually nonlinear and non-Gaussian. Due to the small user scale, some random events and data collection errors will cause outliers. These outliers has a greater impact on the prediction results, which makes traditional prediction methods no longer applicable. In order to solve this problem, this paper uses generalized maximum correntropy criterion as a new loss function to replace the mean squared error loss function in the traditional ELM, and constructs the GMCCELM electricity prediction model. Finally, the historical electricity consumption data of a commercial user in Guangdong province and the corresponding temperature data are applied to the electricity consumption prediction model established in this paper. An actual case is given to illustrate the superiority and effectiveness of the electricity consumption prediction model compared with other existing methods. The prediction results show that, compared with the traditional ELM and other algorithms, the robust prediction model established in this paper can effectively improve the accuracy of electricity consumption forecasting.

Background Availability of numerous oil deposits with a wide variety of the conditions of occurrence, reservoir properties and physical-chemical properties of fluids makes important the need of grouping of development objects and identification of relatively uniform groups by certain criteria. Grouping allows to define similarities and differences between deposits in determining development objects, and to substantiate systems of development. Analysis of large data sets on objects characterized by the measured or estimated parameters is labor-intensive, requiring a lot of time and effort, so there is a question of dimension reduction, i.e. of reducing the original data set to a smaller number of parameters, and grouping of objects into relatively uniform groups. Here the parameters can be selected from the original ones or obtained by calculation and conversion (with the least loss of information about the objects under study). An effective tool of analysis that can help in this matter is the method of principal component analysis. Aims and Objectives Apply the method of principal component analysis to reduce the number of parameters which define objects of study, so that to find effective ways to develop new oil and gas deposits, and fast and correct solution of practical problems through the use of gained summary experience and assessment of similarities and differences of oil and gas reservoirs by combinations of geological-field attributes. Methods The problem of ranking oil deposits in Western Siberia, is solved by the method of principal component analysis, which is a linear transformation of features in a new set of «independent» random values, which are arranged in descending order of their variances, and the main components are determined by the eigenvectors of the correlation matrix obtained based on input parameters characterizing the objects under investigation. Having selected the components that comprise the bulk of the variances, one can calculate them for different types of objects and classify by clusterable points. Grouping of objects of research was carried out using built-in functions of computer statistics programs (a system of statistical analysis Statistica, analytical applications for Microsoft Excel XLStat). The same calculation was performed on the basis of the theory of mathematical statistics. The calculation results coincided. Results The method of principal component analysis allowed selection from the original data set comprising 50 research objects characterized by 12 parameters of six relatively uniform groups using only 4 factors (principal components) which, however, contain much more information than the individual parameters. As a result of the carried out analysis data were systematized and the dimension of the initial population (transition from multidimensional space to the four-dimensional) was reduced, with no significant loss of information about research objects. Conclusion Based on the proposed algorithm, and relatively uniform groups obtained there is a possibility for inclusion of new deposits into one of these groups and selection of the nearest to it analogous object, that will simplify the design of development system and identify ways and means of improving system’s effectiveness.

In today’s society, under the demand for high-quality talents, the professionals cultivated in colleges and universities should not only master professional skills but also need to have the spirit of innovation and entrepreneurial ability in professional aspects. In this paper, firstly, the multivariate statistical analysis method is explained, and the basic principles of the factor analysis model, principal component analysis method and systematic clustering method are given to analyze the data in the following article. Secondly, starting from the necessity of the integration of bi-initiative education and professional education of teacher training majors in colleges and universities, we constructed the 4344 specialized and innovative integrated talent cultivation model of teacher training majors and constructed an evaluation system to evaluate the effectiveness. Finally, based on the evaluation system, the data analysis of the specialized and creative integrated talent cultivation model was carried out using factor analysis, principal component analysis and systematic clustering method. The results show that in the factor analysis, the highest loading value of the first principal factor is 0.917, the contribution rate of the first principal factor is 39.67%, and the loading value of the principal component factor reaches the highest value of 0.925. The clustering analysis is based on the results of the factor analysis, and the respondents are divided into 4 clusters. The number of people in the 2nd category is more than 30 people, which accounts for about 31.41% of the total number of people. The method of multivariate statistical analysis can be used to analyze the data effectively for the specialized and integrated personnel training mode of teacher training in colleges and universities and also gives the path of specialized and integrated personnel training for teacher training.

The wastewater treatment process is characterized by uncertainty, non-linearity, time delay and complexity, and is susceptible to many dynamic factors. Since some key water quality parameters are not available in real time, a Long Short-Term Memory (LSTM) network water quality prediction model based on sparrow search algorithm (SSA-LSTM) and attention mechanism is proposed to solve the problem. In this model, we take historical data as input, constructs models to learn the feature of internal dynamic changes, introduces the attention mechanism, assigns different weights to the hidden state of the LSTM network by mapping weightings with the learning parameter matrix, and uses the SSA to select the optimal hyperparameters for prediction. As high-latitude feature vectors are subject to the curse of dimension, a PCA-LSTM model is further proposed to apply the Principal Component Analysis (PCA) method to the SSA-LSTM model to reduce the dimensionality of the original data. The SSA-LSTM model without the PCA method (NPCA-LSTM) and the PCA-LSTM model are applied to predict wastewater quality and the PCA-LSTM model shows higher predictive ability.

Stall characteristics analyses and stall lift robustness inverse design for high-lift devices of a wide-body commercial aircraft

Principal component analysis is an unsupervised linear dimension reduction method in machine learning. The basic idea of principal component analysis is to project the data of feature space into low dimensional space, and ensure the maximum variance of projection point, which turns the problem into the optimal parameter problem that can make the maximum variance. In the field of machine learning, the gradient ascent method is usually used to solve the optimal parameters, but it is easy to fall into the local maximum trap. In this paper, a principal component analysis method with high- order truncated gradient ascent method is proposed. High- order truncated gradient ascent method is used to replace the gradient ascent method so as to better retain the gradient information and have better global convergence.

In order to save unnecessary energy losses, power companies need to accurately predict electricity consumption. Various classification methods are used for electricity consumption forecasting: traditional time series models, deep neural network models based on Artificial Intelligence, and hybrid models. Most scholars use a single view to forecast electricity consumption. This article predicts electricity consumption from three views: traditional models, deep neural network models, and hybrid models. First, the electricity consumption time series is preprocessed. Then, from three views, models that have achieved good performance in some time series are selected, and then the forecast analysis of the model is given. Finally, the performance of different models in electricity consumption forecasting is evaluated and compared. The results show that the expected performance of the hybrid model is not as good as the traditional model and the deep neural network model in electricity consumption forecasting.

There are limitations for the existing methods to model multivariate time series because that defining the input components is highly difficult. The main purpose of this paper is to expand the principal components analysis (PCA) method to extract the joint information of multiple variables. First, both the linear correlations and the nonlinear correlations are detected to initialize an embedding delay window, which contains enough information for prediction. Then, the PCA method is expanded to extract the joint information of multiple variables in a complex system. Finally, neural network makes predictions on the basis of approximating both the functional relationship between different variables and the map between current state and future state.

This paper proposes an adaptive fuzzy expert system to concurrently estimate and forecast both long-term electricity and natural gas (NG) consumptions with hike in prices. Using a novel procedure, the impact of price hike is incorporated into energy demand modeling. Furthermore, adaptive network-based FIS (ANFIS) is used to model NG consumption in power generation (NGPG). To cope with random uncertainty in small historical data sets, Monte Carlo simulation is used to generate training data for ANFIS. The proposed ANFIS uses electricity consumption data to improve the estimation of total NG consumption. The unique contribution of this paper is three fold. First, it proposes a novel expert system for electricity consumption and NG consumption in end-use sector with hike in prices. Second, it uses ANFIS-Monte Carlo approach for NGPG. Third, electricity consumption is used in ANFIS for improvement of NGPG consumption estimation. A real case study is presented that illustrates the applicability and usefulness of the proposed model where it is applied for joint forecasting of annual electricity and NG consumption with hike in prices.

This paper presents a novel approach to forecast day-ahead electricity consumption for residential households where highly irregular human behaviour plays a significant role. The methodology requires data from fiscal smart meters, which makes it applicable to real scenarios where personal data gathering is not feasible. These data are rarely complete; therefore, a robust combination of machine-learning techniques is used to handle missing data and outliers. The novelty of this method relies on identifying and predicting user electricity consumption behaviour as a procedure to improve the forecasting of the overall electricity consumption of each individual customer. The methodology uses Gaussian mixture clustering to identify behaviour clusters and an eXtreme Gradient Boosting classification (XGBoost) model to predict the day-ahead behaviour pattern. This predicted user behaviour cluster is fed into an Artificial Neural Network (ANN) to enable an improved capturing of the highly unpredictable user conduct for the forecast of electricity consumption. A novel metric, namely the Euclidean Distance-based Accuracy (EDA), is finally proposed to enable a more thorough evaluation of time series classification algorithms. The whole development is tested over 500 residential users placed in a southeastern region of Spain. The results showed that, when the novel approach was used, the MAPEd and NRMSEd were reduced by 7% and 9% respectively, increasing to a 20% and 17% respective reduction for the best cases according to EDA. This methodology sets the basis for massive user-centred analyses, very profitable to any electricity company.

At present, the electricity market reform has entered a deep area, electricity consumption forecasting has become increasingly important, accurate electricity consumption forecasting provides a reference basis and decision-making support for power dispatching and market transactions, and residential power consumption prediction can help users choose appropriate power suppliers and power supply programs according to the market situation, and provide the reliability and economy of power consumption. Residential electricity consumption is complex, affected by many factors and prone to significant noise disturbances, which results in electricity consumption data that is characterized by non-stationary, intermittent and erratic fluctuations. Therefore, using a single model is challenging to accurately predict household electricity consumption. To meet this challenge, this paper designs the structure of this three-step residential electricity consumption forecasting. At the first stage, we first analyzed cumulative effects of temperature on residential electricity consumption, and an hourly temperature correction model combining meteorological factors is constructed, the adjusted hourly temperature data are then entered into the predictive model. We have developed a Variable Modal Decomposition (VMD) data decomposition technique optimized for non-governmental organizational models, which improves the problem of subjectivity in parameter setting in traditional VMD, thus enhancing the performance and accuracy in data decomposition. In the second stage, developed a BiLSTM-AM based integrated deep learning model and dynamically adjusted the weights of the influencing factors by introducing an Attention Mechanism (AM) to enhance the stability of the model, also predict multiple IMF components obtained after the NGO-VMD decomposition, respectively. In the third stage, the training residuals of the BiLSTM-AM model are used as target variables to correct the prediction error in BiLSTM-AM using the XGBoost regression model. A variety of model configurations were constructed using actual data from a coastal province in southern China, and the computational results show that the integrated prediction model exhibits excellent stability and accuracy.