Load Forecasting Model Research Articles

Load forecasting based on dynamic adaptive and adversarial graph convolutional networks

With the widespread integration of renewable energy sources into the power system and the diversification and complexity of the energy consumption patterns, which makes it more difficult to maintain a source-load balance of the power system.In the power systems, the results of the power load forecasting can be used for generator scheduling and load distribution. Therefore, the accurate power load forecasting is critical to operation of the power system. In this paper, a load forecasting model based on a dynamic adaptive and adversarial graph convolutional network (DAAGCN) is proposed, which combines a dynamic adaptive graph generation network (DAGG) and a generative adversarial network (GAN). Firstly, DAGG utilizes integrated time-varying embedding and node embedding to generate dynamic adaptive graphs for inferring spatial–temporal dependencies between different temporal loads. Second, the underlying spatial–temporal prediction model is adversarial trained based on the idea of a zero-sum game. The module parameters are optimized using the L1 loss and the adversarial training loss together as the training objectives of the model. Finally, short-term forecasting of electricity loads was performed on the datasets using DAAGCN, and the results were compared with several other forecasting modules.

GRU combined model based on multi-objective optimization for short-term residential load forecasting

Reliable and accurate short-term forecasting of residential load plays an important role in DSM. However, the high uncertainty inherent in single-user loads makes them difficult to forecast accurately. Various traditional methods have been used to address the problem of residential load forecasting. A single load forecast model in the traditional method does not allow for comprehensive learning of data characteristics for residential loads, and utilizing RNNs faces the problem of long-term memory with vanishing or exploding gradients in backpropagation. Therefore, a gated GRU combined model based on multi-objective optimization is proposed to improve the short-term residential load forecasting accuracy in this paper. In order to demonstrate the effectiveness, GRUCC-MOP is first experimentally tested with the unimproved model to verify the model performance and forecasting effectiveness. Secondly the method is evaluated experimentally with other excellent forecasting methods: models such as DBN, LSTM, GRU, EMD-DBN and EMD-MODBN. By comparing simulation experiments, the proposed GRU combined model can get better results in terms of MAPE on January, April, July, and November load data, so this proposed method has better performance than other research methods in short-term residential load forecasting.

Individual household load forecasting using bi-directional LSTM network with time-based embedding

Accurate individual household load forecasting is essential for effectively managing energy demand and promoting efficient energy consumption. This study evaluates the performance of various deep learning models for individual household load forecasting, specifically using the Smart Grid Smart City (SGSC) dataset. Feature engineering is conducted, producing two distinct sets of features, and the models’ accuracy in predicting individual household loads is assessed using both basic and derived feature sets. The results demonstrate that the T2VBiLSTM model outperforms other models, achieving an average mean absolute percentage error (MAPE) of 74.90% and a root mean square error (RMSE) of 0.433 kW. The incorporation of a wider range of features, including maximum load, minimum load, and load range over time, is emphasized to enhance forecasting accuracy. These features capture long-term load behavior, enabling the models to comprehend complex energy consumption patterns and generate more accurate and reliable predictions. Moreover, limitations in using MAPE as a loss function for load forecasting at the individual customer level are revealed, due to its sensitivity to scale, asymmetry, outliers, and uniform weighting assumption. Alternative loss functions like mean absolute error (MAE) are recommended, as they treat all errors equally and better capture data peaks. While this study focuses on the SGSC dataset, the findings have broader implications for efficiently managing energy demand and promoting energy consumption on a larger scale.

Shore Power System Load Forecasting Model based on IDBO algorithm and PCA-BiLSTM Network

Shore power offers convenient and sustainable power services to ships docking in ports, playing a pivotal role in advancing environmental preservation and the effective utilization of resources. Shore power system loads are susceptible to numerous influencing factors. Therefore, it is crucial to identify a highly accurate load forecasting model to facilitate the judicious allocation of power resources. A short-term shore power system load forecasting model based on the improved dung beetle optimization algorithm (IDBO) and principal component analysis (PCA) in conjunction with the enhanced bidirectional long and short-term memory (BiLSTM) network is proposed in this paper. The IDBO algorithm improves the problem of low convergence accuracy and vulnerability to local optima in DBO. This is achieved by population initialization based on cubic chaotic mapping, amalgamating walrus optimization algorithm, and adaptive t-distribution perturbation strategy. The performance of the BiLSTM, IDBO-BiLSTM, and PCA-IDBO-BiLSTM shore power system load forecasting models are compared through case studies, revealing that the proposed PCA-IDBO-BiLSTM model demonstrates superior predictive capabilities.

Research on the impact of sliding window and differencing procedures on the support vector regression model for load forecasting

Load forecasting is a critical aspect of energy management and grid operations. Machine learning techniques as support vector regression (SVR), have been widely used for load forecasting. However, the effectiveness of SVR is highly dependent on its hyperparameters, including the error sensitivity parameter, penalty factor, and kernel function. Furthermore, as the load data follows a time series pattern, the accuracy of the SVR model is influenced by the data's characteristics. In this regard, the present study aims to investigate the impact of combining the sliding window procedure and differencing the input data on the prediction accuracy of the SVR model. The study utilizes daily maximum load data from the Queensland and Victoria states in Australia. The analyses revealed that while the sliding window procedure had a minimal impact on the prediction results, the differencing of the input data significantly improved the accuracy of the predictions.

Research on long term power load grey combination forecasting based on fuzzy support vector machine

Aiming at the problem of low accuracy of single model in power load forecasting, a method based on fuzzy support vector machine and grey combination forecasting is proposed. The power load data is analyzed and prepared by means of data repair, preprocessing and fuzzy C-means clustering, which is combined with fuzzy support vector machine and grey prediction. The clustering results are taken as the input of the grey prediction model, and the preliminary prediction results are obtained through parameter modification. Then, the results are taken as the input of support vector machine, and the long-term power load prediction is carried out by combining the two models. The experimental results show that the proposed method has excellent performance in predicting long-term power load accurately, and the prediction accuracy is significantly improved compared with the traditional single forecasting model. The application of this method to long-term power load forecasting below 4000 KWH proves its high prediction accuracy and reliability once again. When the clustering category of power load fuzzy clustering is set to 30, the best data clustering effect can be obtained, and the prediction accuracy is further improved.

DBSCAN-based energy users clustering for performance enhancement of deep learning model

Background: Due to rapid progress in the fields of artificial intelligence, machine learning and deep learning, the power grids are transforming into Smart Grids (SG) which are versatile, reliable, intelligent and stable. The power consumption of the energy users is varying throughout the day as well as in different days of the week. Power consumption forecasting is of vital importance for the sustainable management and operation of SG. Methodology: In this work, the aim is to apply clustering for dividing a smart residential community into several group of similar profile energy user, which will be effective for developing and training representative deep neural network (DNN) models for power load forecasting of users in respective groups. The DNN models is composed of convolutional neural network (CNN) followed by LSTM layers for feature extraction and sequence learning respectively. The DNN For experimentation, the Smart Grid Smart City (SGSC) project database is used and its energy users are grouped into various clusters. Results: The residential community is divided into four groups of customers based on the chosen criterion where Group 1, 2, 3 and 4 contains 14 percent, 22 percent, 19 percent and 45 percent users respectively. Almost half of the population (45 percent) of the considered residential community exhibits less than 23 outliers in their electricity consumption patterns. The rest of the population is divided into three groups, where specialized deep learning models developed and trained for respective groups are able to achieve higher forecasting accuracy. The results of our proposed approach will assist researchers and utility companies by requiring fewer specialized deep-learning models for accurate forecasting of users who belong to various groups of similar-profile energy consumption.

Construction of short-term electricity demand streaming forecasting model in demand-side dynamic response

In the context of demand-side dynamic response, the electricity supply-demand relationship undergoes constant changes, and short-term electricity load exhibits strong randomness and volatility, making load conditions challenging to predict. Hence, this paper proposes a short-term electricity demand streaming forecasting model that combines wavelet decomposition with Random Forest to enhance the accuracy of short-term electricity load forecasting. This model establishes a load feature system, utilizing a three-scale wavelet decomposition algorithm to break down the load sequence into several sub-sequences of different frequency bands. Subsequently, Random Forest load forecasting models are separately established for these sub-sequences. The final load prediction is obtained through reconstruction. This approach enables quasi-real-time short-term forecasting analysis of demand-side electricity demand.

Forecasting Electricity Consumption for Accurate Energy Management in Commercial Buildings With Deep Learning Models to Facilitate Demand Response Programs

In the context of rapidly increasing energy demands and environmental concerns, optimizing energy management in commercial buildings is a critical challenge. Smart grids, empowered by advanced Energy Management Systems (EMS), play a pivotal role in addressing this challenge through Demand Side Management (DSM). However, the efficiency of DSM-based building EMS is often limited by the accuracy of load forecasting. This paper addresses this gap by exploring load forecasting models within DSM-based building EMS, focusing on a case study in a commercial building in Ankara, Turkey. Employing Deep Learning (DL) models for load forecasting, we provide inputs for rule-based controllers to enhance energy efficiency. Our major contribution is the development of the ANFIS-IC algorithm, aimed at maximizing demand response participation in commercial buildings. ANFIS-IC, integrating ANFIS controllers with LSTM-based load consumption forecasts, leads to a 33.14% reduction in energy consumption and a 39.22% decrease in energy costs, surpassing the performance of rule-based controllers alone which achieve reductions of 25.34% in energy consumption and 34.03% in energy costs. These findings not only highlight the potential of integrating rule-based controllers with deep learning algorithms but also underscore the importance of accurate load forecasting in improving the effectiveness of DSM-based building EMS.

Cross-level steam load smoothing and optimization in industrial parks using data-driven approaches

This study focuses on the integrated energy production system in industrial parks, addressing the problem of stable load dispatch of equipment under demand fluctuations. A cross-level method for steam load smoothing and optimization is proposed, aiming to achieve stable production and optimal economic performance through three levels of integration: load forecasting, load dispatch, and load regulation. Unlike traditional methods that directly use load forecasting values, heat network elasticity is presented as a buffer between demand and supply. Constraints for minimal changes in equipment load and operational parameters are established for smooth regulation. Industrial cases demonstrate that the load forecasting model has mean absolute percentage errors of 2.44% and 1.68% for medium-pressure and low-pressure steam, respectively, meeting accuracy requirements. The modified supply-side load smoothness is effectively improved by considering heat network elasticity. The method increases boiler efficiency by 1.92%, reducing average coal consumption by 0.92 t/h. Compared to manual operation, the proposed model leads to an average increase of 5.69 MW in power generation and an average reduction of 10.81% in coal-to-electricity ratio. This study verifies the importance of smooth integration across different levels and analyzes the effective response of the proposed method to the uncertainty in load forecasting. The method demonstrates the enormous potential of data-driven methods in achieving safe, economical, and sustainable production in industrial parks.

Net load forecasting method in distribution grid planning based on LSTM network

Distribution grid planning involves multiple nodes, lines, equipment, and other elements. Due to the large scale of the system, there are complex interactions in space. The net load is affected by the load changes of different nodes. If this spatial complexity is not fully considered, the net load prediction results may be inaccurate. Therefore, in order to ensure the effect of net load forecasting, a method of net load forecasting in distribution grid planning based on the Long Short-Term Memory (LSTM) network is proposed. This method fully considers the characteristics of distribution grid planning and constructs a net load forecasting model for distribution grid planning based on the LSTM network. This model selects the 3σ criterion detects and corrects the singular values in the historical load data, and obtains the reasonable maximum time series results of each day; The adaptive noise complete set empirical mode decomposition method is used to decompose the sequence results and generates Intrinsic Mode Function (IMF) components of each time series; According to the component results, a load forecasting model based on LSTM network is constructed, and the initial learning rate and cell number parameters of LSTM network are optimized by improving the Pelican optimization algorithm to improve the precision of load forecasting of LSTM network. The test results show that the method can detect singular values in the data and weaken the impact of grid planning on netload forecasting; It can effectively complete the decomposition of historical load data, and each component after decomposition will not be aliased; The prediction error of net load is less than 1.25%, which can provide a reliable basis for grid planning of distribution network.

A Novel Sequence-to-Sequence-Based Deep Learning Model for Multistep Load Forecasting.

Load forecasting is critical to the task of energy management in power systems, for example, balancing supply and demand and minimizing energy transaction costs. There are many approaches used for load forecasting such as the support vector regression (SVR), the autoregressive integrated moving average (ARIMA), and neural networks, but most of these methods focus on single-step load forecasting, whereas multistep load forecasting can provide better insights for optimizing the energy resource allocation and assisting the decision-making process. In this work, a novel sequence-to-sequence (Seq2Seq)-based deep learning model based on a time series decomposition strategy for multistep load forecasting is proposed. The model consists of a series of basic blocks, each of which includes one encoder and two decoders; and all basic blocks are connected by residuals. In the inner of each basic block, the encoder is realized by temporal convolution network (TCN) for its benefit of parallel computing, and the decoder is implemented by long short-term memory (LSTM) neural network to predict and estimate time series. During the forecasting process, each basic block is forecasted individually. The final forecasted result is the aggregation of the predicted results in all basic blocks. Several cases within multiple real-world datasets are conducted to evaluate the performance of the proposed model. The results demonstrate that the proposed model achieves the best accuracy compared with several benchmark models.

A new Frontier in electric load forecasting: The LSV/MOPA model optimized by modified orca predation algorithm

Electric load forecasting is a vital task for energy management and policy-making. However, it is also a challenging problem due to the complex and dynamic nature of electric load data. In this paper, a novel technique, called LSV/MOPA, has been proposed for electric load forecasting. The technique is a hybrid model that combines the advantages of Long Short-Term Memory (LSTM) and Support Vector Regression (SVR), two powerful artificial intelligence algorithms. The hybrid model is further optimized by a newly Modified Orca Predation Algorithm (MOPA), which enhances the forecasting accuracy and efficiency. The LSV/MOPA model has been applied to historical electric load data from South Korea, covering four regions and 20 years. The LSV/MOPA model has been compared with other state-of-the-art forecasting techniques, including SVR/FFA, LSTM/BO, LSTM-SVR, and CNN-LSTM. The results show that the LSV/MOPA model with minimum average mean absolute percentage deviation error, including 365 in northern region, 12.8 in southern region, 8.6 in central region, and 30.8 in eastern region, provides the best fitting and outperforms the other techniques in terms of the Mean Absolute Percentage Deviation (MAPD) index, achieving lower values for all regions and years. The LSV/MOPA model also exhibits faster convergence and better generalization than the other techniques. This study demonstrates the effectiveness and superiority of the LSV/MOPA model for electric load forecasting and suggests its potential applications in other sectors where accurate forecasting is crucial.

Deciding When to Use a Personalized Model for Load Forecasting

Deciding When to Use a Personalized Model for Load Forecasting

The Power Load Forecasting Model of Combined SaDE-ELM and FA-CAWOA-SVM Based on CSSA

The Power Load Forecasting Model of Combined SaDE-ELM and FA-CAWOA-SVM Based on CSSA

Energy Management System for the Campus Microgrid Using an Internet of Things as a Service (IoTaaS) with Day-ahead Forecasting

In the contemporary energy landscape, characterized by a global commitment to sustainability, the effective management and forecasting of energy consumption play pivotal roles in achieving environmental and economic goals. As nations strive to meet sustainable development targets, optimizing energy use becomes imperative. This paper addresses these challenges by focusing on load forecasting and energy management within the context of a Savona campus microgrid. In this thesis, the NNR algorithm based load profile prediction model was proposed. The development process involved a detailed exploration of the correlation between weather information and electricity consumption. Furthermore, the outputs of the load forecasting model, namely the predicted load profiles, were subsequently utilized in the Energy Management System (EMS) to optimally manage power flows in the campus microgrid using an Internet of things as a service (IoTaaS) with day-ahead forecasting model. The overall results of the model evaluation across all periods reveal a Mean Absolute Error (MAE) of 9.63 kW, a Coefficient of Determination (R2) of 0.79, and a Mean Absolute Percentage Error (MAPE) of 9.02%. These metrics provide a comprehensive assessment of the model's performance across various temperature conditions. The proposed load profile forecasting model was integrated into the Energy Management System (EMS) developed for Savona campus microgrid in Italy. The findings provide a valuable framework for optimizing microgrid operations, aligning with global sustainability objectives.

Application of Gray Expansion Model in Energy Economic Analysis and Load Forecasting

Objective and effective prediction of energy consumption can not only optimize the energy consumption structure, but also provide important information for the government to formulate energy conservation and emission reduction measures. With the development of new energy sources and changes in the global energy consumption structure, historical energy data that are too old may no longer be reliable for forecasting, which leads to a decrease in the amount of information on energy, and the gray theory, which is applicable to “poor information”, has gained attention. Firstly, the optimization of energy economic objectives and transformation path methods at this stage is clarified; then, the DEA-Malmqusit model is used to improve the shortcomings of the traditional model that can only compare different cross-sections at the same time node, and to evaluate and analyze the full-factor multi-indicators of energy enterprises in terms of technological empowerment, environmental dynamics, and economic output efficiency; finally, the LEAPS-based energy system consumption and load capacity prediction model. The results show that the traditional algorithm is not accurate enough and has some deviation when the energy raw data fluctuates a lot. The algorithm proposed in this paper still gives a better prediction, predicting a city’s carbon emission to be 65,240,100 tons in 2024, with a 3.6% increase in energy output year by year.

A Review of Deep Transfer Learning Strategy for Energy Forecasting

Over the past decades, energy forecasting has attracted many researchers. The electrification of the modern world influences the necessity of electricity load, wind energy, and solar energy forecasting in power sectors. Energy demand increases with the increase in population. The energy has inherent characteristics like volatility and uncertainty. So, the design of accurate energy forecasting is a critical task. The electricity load, wind, and solar energy are important for maintaining the energy supply-demand equilibrium non-conventionally. Energy demand can be handled effectively using accurate load, wind, and solar energy forecasting. It helps to maintain a sustainable environment by meeting the energy requirements accurately. The limitation in the availability of sufficient data becomes a hindrance to achieving accurate energy forecasting. The transfer learning strategy supports overcoming the hindrance by transferring the knowledge from the models of similar domains where sufficient data is available for training. The present study focuses on the importance of energy forecasting, discusses the basics of transfer learning, and describes the significance of transfer learning in load forecasting, wind energy forecasting, and solar energy forecasting. It also explores the reviews of work done by various researchers in electricity load forecasting, wind energy forecasting, and solar energy forecasting. It explores how the researchers utilized the transfer learning concepts and overcame the limitations of designing accurate electricity load, wind energy, and solar energy forecasting models.

Short-Term Load Forecasting Based on CNN-Bilstm Considering Load Time-Varying Trend Mapping

Background:: Accurate short-term load forecasting is an important guarantee for the safety, stability, economic and efficient operation of power systems. Objective:: In order to effectively improve the forecasting accuracy, this paper proposes a hybrid forecasting model of convolutional neural network (CNN) and bi-directional long short-term memory (BiLSTM) neural network considering load time-varying trend mapping model (M). Methods:: Firstly, the time-varying features of the load curve are analyzed, and a mapping model is established to characterize the load time-varying trend. The features of the load time-varying trend are extracted, and they are quantified into a mathematical model. Secondly, the feature set is reconstructed through data migration. Then, the reconstructed feature set is input into the CNN-BiLSTM hybrid model. In the hybrid model, CNN is used to extract the features from data again to form a new feature vector, and then the feature vector is input into BiLSTM for forecasting. Result:: The power load data set from the New England in United States is used to simulate and verify the correctness and validity of the proposed method. Conclusion:: With the comparison of the forecasting results between different load forecasting models, the results show that the forecasting accuracy of the proposed method is higher and it is verified that the load time-varying trend mapping method proposed can improve the forecasting accuracy of different models in varying degrees.

The effect of electricity consumption determinants in household load forecasting models

Usually, household electricity consumption fluctuates, often driven by several electrical consumption determinants such as income, household size, and price. Recently, research studies on the investigation of predictor variables in household electricity consumption have increased especially in the developing and newly industrialized countries. However, the studies just focus on identifying the predictor variables of household electricity consumption that influence load forecasting models. In Tanzania, for instance, scholars found that using the “income” determinant improves the performance of a forecasting model. The scholars suggest without any empirical bases that adding more predictor variables would have improved the accuracy of the model. This study aims to analyze the effect of the number of predictor variables on household load forecasting performance based on Tanzania’s data. Nonlinear regression based on a Weibull function and multivariate adaptive regression splines approaches are used for this purpose. Our findings indicate that income, household size, and number of appliances are common predictor variables of household consumption in developing countries. The measured forecasting root-mean-square error (RMSE) when using income, household size, and the number of appliances is 0.8244, 1.2314, and 0.9868, respectively. Finally, we forecasted load using all three determinants and the RMSE dropped to 0.7031. Having obtained the smaller value of RMSE when all predictors are used reveals that the inclusion of all three predictor variables in load forecasting leads to a significant decrease in RMSE by 14.73%. Therefore, the study recommends using multiple predictor variables in load forecasting models to increase accuracy.