Short-term Load Prediction Research Articles

Demand-Side Electricity Load Forecasting Based on Time-Series Decomposition Combined with Kernel Extreme Learning Machine Improved by Sparrow Algorithm

With the rapid development of new power systems, power usage stations are becoming more diverse and complex. Fine-grained management of demand-side power load has become increasingly crucial. To address the accurate load forecasting needs for various demand-side power consumption types and provide data support for load management in diverse stations, this study proposes a load sequence noise reduction method. Initially, wavelet noise reduction is performed on the multiple types of load sequences collected by the power system. Subsequently, the northern goshawk optimization is employed to optimize the parameters of variational mode decomposition, ensuring the selection of the most suitable modal decomposition parameters for different load sequences. Next, the SSA–KELM model is employed to independently predict each sub-modal component. The predicted values for each sub-modal component are then aggregated to yield short-term load prediction results. The proposed load forecasting method has been verified using actual data collected from various types of power terminals. A comparison with popular load forecasting methods demonstrates the proposed method’s higher prediction accuracy and versatility. The average prediction results of load data in industrial stations can reach RMSE = 0.0098, MAE = 0.0078, MAPE = 1.3897%, and R2 = 0.9949. This method can be effectively applied to short-term load forecasting in multiple types of power stations, providing a reliable basis for accurate demand-side power load management and decision-making.

A hybrid electricity load prediction system based on weighted fuzzy time series and multi-objective differential evolution

With advances in science and technology, the demand for electricity is increasing dramatically. Consequently, reliable short-term power load prediction is critical to ensure the safety, efficiency, and cost-effectiveness of electricity grids. However, owing to the volatility and randomness of power load time series, traditional forecasting models have struggled to meet the current power system requirements for load prediction precision and stability. In light of this, this study proposes a new hybrid short-term electricity load prediction system that combines the variational modal decomposition (VMD) technique, multi-objective differential evolutionary algorithm (MODE), and weighted fuzzy time series (WFTS) prediction model to increase load prediction precision and stability. Four Belgian datasets (spring, summer, autumn, and winter) were used for short-term power load prediction experiments and were compared with prevalent prediction methods. The experimental results indicate that the designed system obtains MAPESpring=0.6670%, MAPESummer=0.5835%, MAPEAutumn=0.7049%, MAPEWinter=0.7123% and STDSpring=60.2575, STDSpring=62.6573, STDSpring=72.7393, STDSpring=71.5549, which are obviously superior to the comparison models in terms of prediction accuracy and stability.

Multi-prediction of electric load and photovoltaic solar power in grid-connected photovoltaic system using state transition method

In the grid-connected photovoltaic system (GPVS), due to characteristics of fluctuation and intermittency for photovoltaic solar power, and high randomness for electric load, it is of great difficulty for integrating photovoltaic solar power into power grid. Therefore, an accurate prediction of short-term electric load and photovoltaic solar power is of great importance for balancing supply and demand. Currently, numerous isolated models about the forecasting of electric load and photovoltaic solar power have emerged, while the coupling effect between them has been hardly considered and lower stability of existing methods brings great difficulty in providing reliable predictions at practical applications. To address this gap, this paper proposes an interpretable multi-prediction model for short-term (day-ahead) electric load and photovoltaic solar power forecasting. In the framework, a non-parametric functional principal component analysis (FPCA) is constructed to extract the overall trend and identify dominant modes of variation in the daily electric load and photovoltaic solar power data. Furthermore, state transition matrix is proposed to comprehensively interpret the coupling effect, with which a novel multi-prediction strategy that takes advantage of coupling effect is further introduced, where the Maximum Likelihood Estimation (MLE) is employed to estimate unknown parameters. Moreover, data from California Independent System Operator (ISO) is utilized to investigate the performance of proposed method, and its results are compared with those from other widely-used techniques. Results show that the proposed method can increase prediction accuracy of electric load and photovoltaic solar power by 16.84% and 10.57%, respectively, with narrow fluctuations and reasonable computational cost (211.11 s), demonstrating that it can provide better predictions in terms of prediction accuracy, stability and applicability.

Short-Term Prediction of Multi-Energy Loads Based on Copula Correlation Analysis and Model Fusions.

To improve the accuracy of short-term multi-energy load prediction models for integrated energy systems, the historical development law of the multi-energy loads must be considered. Moreover, understanding the complex coupling correlation of the different loads in the multi-energy systems, and accounting for other load-influencing factors such as weather, may further improve the forecasting performance of such models. In this study, a two-stage fuzzy optimization method is proposed for the feature selection and identification of the multi-energy loads. To enrich the information content of the prediction input feature, we introduced a copula correlation feature analysis in the proposed framework, which extracts the complex dynamic coupling correlation of multi-energy loads and applies Akaike information criterion (AIC) to evaluate the adaptability of the different copula models presented. Furthermore, we combined a NARX neural network with Bayesian optimization and an extreme learning machine model optimized using a genetic algorithm (GA) to effectively improve the feature fusion performances of the proposed multi-energy load prediction model. The effectiveness of the proposed short-term prediction model was confirmed by the experimental results obtained using the multi-energy load time-series data of an actual integrated energy system.

Short-term cooling and heating loads forecasting of building district energy system based on data-driven models

Accurate forecasting of cooling and heating loads is critical for optimizing the energy usage of devices and planning for energy storage in building district energy systems (BDESs). Data-driven models offering high prediction accuracy and efficiency are receiving significant attention for addressing short-term load prediction problems. However, load forecasting for district building levels, as well as interpretable studies of deep learning models, are still the minority of existed research. To satisfy the cooling and heating loads forecasting of an actual BDES, a framework that includes raw data acquisition, data processing, model development, and evaluation is developed and analyzed. The performance of a multi-input-multi-output prediction strategy evaluated using five data-driven models, namely extreme gradient boost (XGB), long short-term memory (LSTM), gated recurrent units (GRU), and attention-added LSTM/GRU, is investigated. The results show that XGB performs the best, with CV-RMSE values of 14.51% and 11.95% for the one-hour-ahead forecasting of cooling and heating loads, respectively. In terms of 24-hour ahead prediction, the most accurate condition is “48–24” attention-added LSTM in both cooling and heating loads forecasting, where the CV-RMSE values recorded are 26.61% and 27.58%, respectively. The accuracy and interpretability of the models improved after an attention mechanism is introduced. The most significant enhancement in the prediction accuracy is reflected by a reduction in the CV-RMSE by 2.01%. The visualized heat maps of attention weights indicate the mechanism by which the models learn and extract the load patterns, where LSTM and GRU indicate different priorities. Further analysis of the attention weight distribution reveals the effects of building thermal inertia and load periodicity.

Daily load prediction study on SCASL and BiGRU models

In the short-term power load prediction, the environmental uncertainty will have an impact on the prediction accuracy and increase the complexity of the prediction. To deal with this problem, the paper proposes a bidirectional gated recurrent unit (BiGRU) network model for and sine cosine optimization algorithm with self-learning strategy and Levy flight (SCASL). With real power load data as a data set, high correlation parameters are selected through the Pearson correlation coefficient analysis as input, using the SCASL algorithm and nonlinear weight factor. Finally, the key time points in capturing the historical information of the load changes are established according to the optimization parameters, and BiGRU model is used to learn to predict its accuracy. The experimental results show that the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) of BiGRU model are better than the comparison model gated recurrent unit (GRU) network model. The final results show that the prediction accuracy of the power load system has been significantly improved.

Evaluating the EEMD-LSTM model for short-term forecasting of industrial power load: A case study in Vietnam

This paper presents the effectiveness of the ensemble empirical mode decomposition-long short-term memory (EEMD-LSTM) model for short term load prediction. The prediction performance of the proposed model is compared to that of three other models (LR, ANN, LSTM). The contribution of this research lay in developing a novel approach that combined the EEMD-LSTM model to enhance the capability of industrial load forecasting. This was a field where there had been limited proposals for improvement, as these hybrid models had primarily been developed for other industries such as solar power, wind power, CO2 emissions, and had not been widely applied in industrial load forecasting before. First, the raw data was preprocessed using the IQR method, serving as the input for all four models. Second, the processed data was then used to train the four models. The performance of each model was evaluated using regression-based metrics such as mean absolute error (MAE) and mean squared error (MSE) to assess their respective output. The effectiveness of the EEMD-LSTM model was evaluated using Seojin industrial load data in Vietnam, and the results showed that it outperformed other models in terms of RMSE, n-RMSE, and MAPE errors for both 1-step and 24-step forecasting. This highlighted the model's capability to capture intricate and nonlinear patterns in electricity load data. The study underscored the significance of selecting a suitable model for electricity load forecasting and concluded that the EEMD-LSTM model was a dependable and precise approach for predicting future electricity assets. The model's robust performance and accurate forecasts showcased its potential in assisting decision-making processes in the energy sector.

Short-Term Load Forecasting Based on Improved VMD and KELM

To address the problem of low accuracy of short-term load prediction in the past, this thesis uses a prediction model with neural network LSTM as the main body and constructs an adaptive and dynamically adjusted rolling load prediction model to meet our load prediction needs through VMD decomposition, kernel limit learning machine, and correlation analysis techniques. To reduce the maximum load error and the overall result error, a correction algorithm is proposed to improve the VMD to minimize the impact of load decomposition; after that, the model is used to analyze a load of a day in a region to verify the superiority of the prediction model. The example shows that the load forecasting method can help to make more accurate predictions of short-term loads.

Multifeature Short-Term Power Load Forecasting Based on GCN-LSTM

With the construction of a new-type power system under the China “double carbon” target and the increasing diversification of the energy demand on the user side, the short-term load forecasting of the power system is facing new challenges. To fully exploit the massive information contained in data, based on the graph convolutional network (GCN) and long short-term memory network (LSTM), this paper presents a new short-term load forecasting method for power systems considering multiple factors. The Spearman rank correlation coefficient was used to analyse the correlation between load and meteorological factors, and a model including meteorology, dates, and regions was established. Secondly, GCN and LSTM are jointly used to extract the spatial and temporal characteristics of massive data, respectively, and finally achieve short-term power load prediction. Historical electrical load data from 2020 to 2022 public data of a real industrial park in southern China were selected to verify the validity of the proposed method from the aspects of forecasting accuracy, feature dimension, and training time.

Support Vector Regression untuk Prediksi Beban Listrik Jangka Pendek Menggunakan Metode Fraktal

In this era of globalization, electricity is one of the essential needs in human life. Electricity load prediction plays a crucial role in designing supply-demand operations to avoid losses from various aspects. In this study, short-term electricity load prediction is conducted per 30 minutes, aiming to achieve minimum prediction errors. Support Vector Regression (SVR) is used as the machine learning method for data classification, and fractal method is employed for dimension calculation and feature extraction from historical data. The results of this research are as follows: In the first experiment, short-term prediction was conducted without using the fractal method, resulting in a Mean Absolute Percentage Error (MAPE) of 2.85%. In the second experiment, short-term prediction was performed using the dataset that had undergone fractal calculation and feature extraction, leading to a lower MAPE of 2.32%. The prediction results using the fractal method obtained a lower MAPE compared to the non-fractal approach. Fractal significantly influences the calculation of short-term electricity load prediction using Support Vector Regression (SVR).

TS2ARCformer: A Multi-Dimensional Time Series Forecasting Framework for Short-Term Load Prediction

Accurately predicting power load is a pressing concern that requires immediate attention. Short-term load prediction plays a crucial role in ensuring the secure operation and analysis of power systems. However, existing research studies have limited capability in extracting the mutual relationships of multivariate features in multivariate time series data. To address these limitations, we propose a multi-dimensional time series forecasting framework called TS2ARCformer. The TS2ARCformer framework incorporates the TS2Vec layer for contextual encoding and utilizes the Transformer model for prediction. This combination effectively captures the multi-dimensional features of the data. Additionally, TS2ARCformer introduces a Cross-Dimensional-Self-Attention module, which leverages interactions across channels and temporal dimensions to enhance the extraction of multivariate features. Furthermore, TS2ARCformer leverage a traditional autoregressive component to overcome the issue of deep learning models being insensitive to input scale. This also enhances the model’s ability to extract linear features. Experimental results on two publicly available power load datasets demonstrate significant improvements in prediction accuracy compared to baseline models, with reductions of 43.2% and 37.8% in the aspect of mean absolute percentage error (MAPE) for dataset area1 and area2, respectively. These findings have important implications for the accurate prediction of power load and the optimization of power system operation and analysis.

Transfer learning-based adaptive recursive neural network for short-term non-stationary building heating load prediction

Building energy consumption is a non-stationary time series, and its distribution law changes over time. Traditional machine-learning models are prone to model shift, which leads to a reduction of their prediction accuracy when applied to building energy consumption forecasting. Therefore, in this paper, an adaptive neural network framework is proposed for non-stationary building energy consumption prediction based on transfer learning, in which the training datasets are divided into the most dissimilar periods, and based on the transfer learning mechanism, the different periods of data are learned to obtain the minimum overall loss to improve the model generalization. Taking LSTM and GRU models as examples, adaptive long short-term memory (adaLSTM) and adaptive gated recurrent unit (adaGRU) building energy consumption prediction models are established. The models were trained and verified using heating load data from Xi'an, China. The results show that compared with LSTM model, the coefficient of determination, root mean square error, the coefficient of variation of the root mean squared error and mean absolute error of the adaLSTM model were improved by 0.61%, 37.78%, 38.05% and 30.69%, respectively, and the over-fitting degree was reduced by 227.7%. Compared with the traditional GRU model, the corresponding evaluation indexes of adaGRU were improved by 2.50%, 70.58%, 70.64% and 68.83%, respectively, and the over-fitting degree was improved by 505.7% points. The adaptive recurrent neural network framework proposed in this paper is a generalized approach which can be applied to other non-stationary time series prediction models.

An innovative model for electrical load forecasting: A case study in Australia

Short-term load prediction has always played an increasingly important part in power system administration, load dispatch, and energy transfer scheduling. However, how to build a novel model to improve the accuracy of load forecasts is not only an extremely challenging problem but also a concerning problem for the power market. Specifically, the individual model pays no attention to the significance of data selection, data preprocessing, and model optimization. So these models cannot always satisfy the time series forecasting’s requirements. With these above-mentioned ignored factors considered, to enhance prediction accuracy and reduce computation complexity, in this study, a novel and robust method were proposed for multi-step forecasting, which combines the power of data selection, data preprocessing, artificial neural network, rolling mechanism, and artificial intelligence optimization algorithm. Case studies of electricity power data from New South Wales, Australia, are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed model has significantly increased the accuracy of load prediction in all quarters. As a result, the proposed method not only is simple, but also capable of achieving significant improvement as compared with the other forecasting models, and can be an effective tool for power load forecasting.

Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism

This study proposes a new approach for short-term power load forecasting using a combination of convolutional neural networks (CNN), long short-term memory (LSTM), and attention mechanisms to address the issue of information loss due to excessively long input time series data. The objective is to enhance the accuracy of short-term power load prediction, which is crucial for efficient energy management. The study analyzes the relationship between the target load and the collected parameters, identifying the most influential factors using Pearson correlation coefficient analysis. A one-dimensional CNN layer is utilized to extract high-dimensional features from the input data, followed by an LSTM layer that captures temporal correlations within the historical sequences. Finally, an attention mechanism is introduced to optimize the weight of the LSTM output, enhance the influence of key information, and optimize the overall prediction model. The performance of the proposed model is evaluated using two benchmark models based on mean absolute percentage error (MAPE), root mean square error (RMSE), and mean absolute error (MAE) metrics. The results show that the CNN-LSTM-A model outperforms the traditional LSTM model regarding power load prediction accuracy for two thermal power units, with an improvement of 7.3% and 5.7%, respectively, indicating superior performance. Therefore, this study demonstrates the effectiveness of the proposed CNN-LSTM-A model for short-term power load forecasting, which has potential applications in the energy industry. In conclusion, the proposed approach can improve the accuracy of power load forecasting, leading to more efficient energy management and cost savings. Additionally, the study highlights the importance of incorporating attention mechanisms into traditional LSTM models for power load forecasting, as it helps to optimize the weight of the LSTM output and improve the accuracy of the predictions. The proposed CNN-LSTM-A model can be potentially useful for energy companies and policymakers in making informed decisions regarding energy production and consumption. Overall, this study provides a valuable contribution to power load forecasting, and the proposed approach could be extended to other areas of time-series forecasting in the future.

An Ensemble Deep Learning Model for Provincial Load Forecasting Based on Reduced Dimensional Clustering and Decomposition Strategies

The accurate prediction of short-term load is crucial for the grid dispatching department in developing power generation plans, regulating unit output, and minimizing economic losses. However, due to the variability in customers’ electricity consumption behaviour and the randomness of load fluctuations, it is challenging to achieve high prediction accuracy. To address this issue, we propose an ensemble deep learning model that utilizes reduced dimensional clustering and decomposition strategies to mitigate large prediction errors caused by non-linearity and unsteadiness of load sequences. The proposed model consists of three steps: Firstly, the selected load features are dimensionally reduced using singular value decomposition (SVD), and the principal features are used for clustering different loads. Secondly, variable mode decomposition (VMD) is applied to decompose the total load of each class into intrinsic mode functions of different frequencies. Finally, an ensemble deep learning model is developed by combining the strengths of LSTM and CNN-GRU deep learning algorithms to achieve accurate load forecasting. To validate the effectiveness of our proposed model, we employ actual residential electricity load data from a province in northwest China. The results demonstrate that the proposed algorithm performs better than existing methods in terms of predictive accuracy.

Intelligent grid load forecasting based on BERT network model in low-carbon economy

In recent years, the reduction of high carbon emissions has become a paramount objective for industries worldwide. In response, enterprises and industries are actively pursuing low-carbon transformations. Within this context, power systems have a pivotal role, as they are the primary drivers of national development. Efficient energy scheduling and utilization have therefore become critical concerns. The convergence of smart grid technology and artificial intelligence has propelled transformer load forecasting to the forefront of enterprise power demand management. Traditional forecasting methods relying on regression analysis and support vector machines are ill-equipped to handle the growing complexity and diversity of load forecasting requirements. This paper presents a BERT-based power load forecasting method that leverages natural language processing and image processing techniques to enhance the accuracy and efficiency of transformer load forecasting in smart grids. The proposed approach involves using BERT for data preprocessing, analysis, and feature extraction on long-term historical load data from power grid transformers. Multiple rounds of training and fine-tuning are then conducted on the BERT architecture using the preprocessed training datasets. Finally, the trained BERT model is used to predict the transformer load, and the predicted results are compared with those obtained based on long short-term memory (LSTM) and actual composite values. The experimental results show that compared with LSTM method, the BERT-based model has higher short-term power load prediction accuracy and feature extraction capability. Moreover, the proposed scheme enables high levels of accuracy, thereby providing valuable support for resource management in power dispatching departments and offering theoretical guidance for carbon reduction initiatives.

Short-term and Long-term Electric Load Prediction Using New Machine Learning Techniques with Temperature and Solar Altitude Angle

Short-term and Long-term Electric Load Prediction Using New Machine Learning Techniques with Temperature and Solar Altitude Angle

A Short-term Electricity Load Forecasting Method Based on WOA-BiLSTM-Attention

Under the “double carbon” policy, it is time to build a new green and safe power system. Special attention should be paid to the low accuracy in short-term power load prediction. Therefore, this paper presents a technique for anticipating short-term power demand based on a bidirectional long short-term memory network with a whale-optimized attention mechanism (WOA-BiLSTM-Attention), which is used to forecast and analyze the measured power values in a certain area. The experimental findings reveal that the suggested technique has much greater prediction accuracy and convergence speed, as well as superior stability, when compared to LSTM, BiLSTM, and BiLSTM based on attention mechanism, making it a good reference for power system planning and stability.

A novel short-term household load forecasting method combined BiLSTM with trend feature extraction

Aiming to reduce the short-term household load prediction error caused by small load scale and differently residential electricity consumption behavior, a novel hybrid forecasting model based on wavelet threshold denoising (WTD), variational mode decomposition (VMD) and bidirectional long short-term memory (BiLSTM) network is proposed in this paper. In this hybrid model, WTD is used to denoise the original data firstly. Then trend feature is extracted by VMD. Finally the trend feature and historical load data are put into BiLSTM model for training and testing. The prediction effect of the proposed model is demonstrated with experiment using total household electricity consumption in the United States in a region, and comparison with common short-term household load forecasting models are presented. The experiment results show that the hybrid model improves the accuracy of short-term household load forecasting by providing more stable and more precise forecast under trend feature extraction.

An ANFIS-Fuzzy Tree-GA Model for a Hospital’s Electricity Purchasing Decision-Making Process Integrated with Virtual Cost Concept

In deregulated electricity markets, accurate load and price prediction play an essential role in the Demand Response (DR) context. Although electrical load and price demonstrate a strong correlation which is not linear, price prediction may be a task much more challenging than load prediction due to several factors. The volatility of electricity price compared to load makes price prediction a complex procedure. To perform purchasing decisions commercial consumers may rely on short term price and load prediction. A system which combines Adaptive Neuro-Fuzzy Systems (ANFIS) which predict Load Marginal Prices (LMPs) and electricity consumption is presented in this study. Furthermore, the Virtual Cost (VC) concept, which is the sum of the products between the predicted hourly consumption values and their respective predicted LMPs is introduced. Virtual Cost is assessed with a Fuzzy Decision Tree (FDT) compared to a threshold set by the customer. If needed, the amount of electrical energy that a healthcare facility must purchase at every hour of the day may be scheduled using Genetic Algorithm (GA) to meet the threshold criterion. This hybrid model proved economically beneficial for the facility, which is of great importance since the saved resources may be utilized to improve its infrastructures or for other purposes with social impact. The novelty of the proposed method is the utilization of ANFIS, Fuzzy Decision Trees and Genetic Algorithms combined as tools to improve the hospital’s energy and economic efficiency, achieving a reduction of the electricity costs up to 21.95 percent. The contribution of the study is to provide a reliable decision-making tool to everyone who participates in the electricity market in order to perform profitable energy scheduling automatically and accurately.