Short-term Load Forecasting Model Research Articles

From Time-Series to Hybrid Models: Advancements in Short-Term Load Forecasting Embracing Smart Grid Paradigm

This review paper is a foundational resource for power distribution and management decisions, thoroughly examining short-term load forecasting (STLF) models within power systems. The study categorizes these models into three groups: statistical approaches, intelligent-computing-based methods, and hybrid models. Performance indicators are compared, revealing the superiority of heuristic search and population-based optimization learning algorithms integrated with artificial neural networks (ANNs) for STLF. However, challenges persist in ANN models, particularly in weight initialization and susceptibility to local minima. The investigation underscores the necessity for sophisticated predictive models to enhance forecasting accuracy, advocating for the efficacy of hybrid models incorporating multiple predictive approaches. Acknowledging the changing landscape, the focus shifts to STLF in smart grids, exploring the transformative potential of advanced power networks. Smart measurement devices and storage systems are pivotal in boosting STLF accuracy, enabling more efficient energy management and resource allocation in evolving smart grid technologies. In summary, this review provides a comprehensive analysis of contemporary predictive models and suggests that ANNs and hybrid models could be the most suitable methods to attain reliable and accurate STLF. However, further research is required, including considerations of network complexity, improved training techniques, convergence rates, and highly correlated inputs to enhance STLF model performance in modern power systems.

ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting.

Short-term load forecasting (STLF) is challenging due to complex time series (TS) which express three seasonal patterns and a nonlinear trend. This article proposes a novel hybrid hierarchical deep-learning (DL) model that deals with multiple seasonality and produces both point forecasts and predictive intervals (PIs). It combines exponential smoothing (ES) and a recurrent neural network (RNN). ES extracts dynamically the main components of each individual TS and enables on-the-fly deseasonalization, which is particularly useful when operating on a relatively small dataset. A multilayer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS. To improve the internal TS representation and thus the model's performance, RNN learns simultaneously both the ES parameters and the main mapping function transforming inputs into forecasts. We compare our approach against several baseline methods, including classical statistical methods and machine learning (ML) approaches, on STLF problems for 35 European countries. The empirical study clearly shows that the proposed model has high expressive power to solve nonlinear stochastic forecasting problems with TS including multiple seasonality and significant random fluctuations. In fact, it outperforms both statistical and state-of-the-art ML models in terms of accuracy.

Fuzzy Models for Short Term Power Forecasting in Palestine

Short-Term Load Forecasting (STLF) is needed to efficiently manage the power systems. In this paper, two kinds of models that depend on the Fuzzy based techniques are developed to represent the STLF models in Palestine. Different types of these models have been developed using the available data sets that include the past electric load values and the climatic variables as inputs. It is shown that the climatic variables have a major effect on the predicted load. Various optimization techniques are used to develop the proposed models including hybrid and Backpropagation optimization techniques, Subtractive Clustering, and combining the Subtractive Clustering and Hybrid optimization techniques. The obtained results indicate the efficiency of the proposed models using the time and weather data.

Contextually enhanced ES-dRNN with dynamic attention for short-term load forecasting

In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models.

Privacy-Preserving and Hierarchically Federated Framework for Short-Term Residential Load Forecasting

Short-term Load Forecasting (STLF) plays a fundamental role in modern energy systems. This paper proposes a privacy-preserving STLF framework for residential energy users. The framework enables users to collaboratively train an STLF model without exchanging their load data. The system works in two stages: (i) a federated user clustering method is developed to divide the users into multiple clusters based on load pattern similarity. Instead of aggregating the users’ data to do clustering, the developed method determines the user clusters in a distributed manner. (ii) after the user clustering stage, a hierarchically federated STLF model training is developed, which applies federated learning principles to facilitate intra-and inter-user cluster model training. In the process, the individual users also do not expose their load data. Further, an asynchronous communication mechanism is designed and integrated into the framework, making it high fault-tolerant and adaptable to the residential environment with communication uncertainties. Comprehensive experiments based on a real Australian load dataset are conducted to validate the system. The simulation results show that the proposed framework can effectively train the load forecasting model with a fast coverage rate and achieve up to 37.25% prediction accuracy improvement (in terms of the Mean Absolute Percentage Error metric) compared with the other benchmark methods.

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.

Deep learning time pattern attention mechanism-based short-term load forecasting method

Accurate load forecasting is crucial to improve the stability and cost-efficiency of smart grid operations. However, how to integrate multiple significant factors for enhancing load forecasting performance is insufficiently investigated in previous studies. To fill the gap, this study proposes a novel hybrid deep learning model for short-term load forecasting. First, the long short-term memory network is utilized to capture patterns from historical load data. Second, a time pattern attention (TPA) mechanism is incorporated to improve feature extraction and learning capabilities. By discerning valuable features and eliminating irrelevant ones, the TPA mechanism enhances the learning process. Third, fully-connected layers are employed to integrate external factors such as climatic conditions, economic indicators, and temporal aspects. This comprehensive approach facilitates a deeper understanding of the impact of these factors on load profiles, leading to the development of a highly accurate load forecasting model. Rigorous experimental evaluations demonstrate the superior performance of the proposed approach in comparison to existing state-of-the-art load forecasting methodologies.

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.

Research on short-term load forecasting of power system based on IWOA-KELM

A short-term power load forecasting (STPLF) model based on the Improved Whale Optimization Algorithm (IWOA) optimized Kernel Extreme Learning Machine (KELM) is proposed to address the problems of high randomness and low forecasting accuracy of electricity loads. The KELM model is constructed, and the IWOA is used to optimize the core and penalty parameters of the KELM to establish the IWOA-KELM electricity load forecasting model. Combined with the actual data of a certain region, the forecasting analysis results show that the convergence speed and forecasting accuracy of the method are greatly improved compared with IWOA-BP, IWOA-SVM and IWOA-ELM forecasting methods.

Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead

Short-term load forecasting (STLF) is critical for the energy industry. Accurate predictions of future electricity demand are necessary to ensure power systems’ reliable and efficient operation. Various STLF models have been proposed in recent years, each with strengths and weaknesses. This paper comprehensively reviews some STLF models, including time series, artificial neural networks (ANNs), regression-based, and hybrid models. It first introduces the fundamental concepts and challenges of STLF, then discusses each model class’s main features and assumptions. The paper compares the models in terms of their accuracy, robustness, computational efficiency, scalability, and adaptability and identifies each approach’s advantages and limitations. Although this study suggests that ANNs and hybrid models may be the most promising ways to achieve accurate and reliable STLF, additional research is required to handle multiple input features, manage massive data sets, and adjust to shifting energy conditions.

An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting

An improved encoder-decoder-based CNN model for probabilistic short-term load and PV forecasting

Hybrid short-term load forecasting using CGAN with CNN and semi-supervised regression

Accurate short-term load forecasting (STLF) is essential to improve secure and economic operation of power systems. In this paper, a hybrid STLF model using the conditional generative adversarial network (CGAN) with convolutional neural network (CNN) and semi-supervised regression is proposed to improve the accuracy of STLF. Firstly, a conditional label matrix with relevant factors is constructed as the conditional labels of CGAN. The grey weighted correlation method is applied to generate high-quality similar days as one of the labels. The input data with conditional labels and load time series are decomposed into several sub-modes by the variational mode decomposition (VMD), which transforms the load forecasting into several sub-forecasting. Then, the CGAN generator is to capture the internal feature of each mode with the CNN and generate fake samples, while the CGAN discriminator is modified with a semi-supervised regression layer to extract the nonlinear and dynamic behaviors of the dataset and perform precise STLF. The final forecasting results are obtained by aggregating the results of all sub-mode. The generator and discriminator of the CGAN form a min–max game to improve the sample generation ability and reduce forecasting errors. The simulation results show that the STLF accuracy with the proposed model is significantly improved.

A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting

AbstractNumerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information. However, less attention has been paid to the input data size and output dimensions in STLF. To address these two issues, an STLF model is proposed based on output dimensions using only load data. First, the load data's long‐term behavior (trend and seasonality) is extracted through the long short‐term memory network (LSTM), followed by convolution to obtain the load data's non‐stationarity. Then, using the self‐attention mechanism (SAM), the crucial input load information is emphasized in the forecasting process. The calculation example shows that the proposed algorithm outperforms LSTM, LSTM‐based SAM, and CNN‐GRU‐based SAM by more than 10% in eight different buildings, demonstrating its suitability for forecasting with only load data. Additionally, compared to earlier research utilizing two well‐known public data sets, the MAPE is optimized by 2.2% and 5%, respectively. Also, the method has good prediction accuracy for a wide variety of time granularities and load aggregation levels, so it can be applied to various load forecasting scenarios and has good reference significance for load forecasting instrumentation.

An exposition on the prediction of load on a Smart Grid

Smart grids depend on AI-based load forecasting to estimate future power demand (AI). Deep learning is especially important in smart grid load forecasting with neural networks (ANN). Processing time and data are needed to count smart grid deep learning. Combining data would speed load projections. The bottleneck strategy has been abandoned to attain this precision. Keeping the lights on requires short-term electricity demand prediction. But, the load’s intricacy and volatility make it fun to predict. EEMD breaks the load into many frequency-dependent components of different strengths. MLR predicts low-frequency regularities, while LSTM neural networks predict high-frequency components. Computational extent is unchanged. Despite its varied aggregation scope, the electric grid’s large data can be used to create the most effective deep learning models for Short-term Load Forecasting (STLF) in electrical networks. Hence, a suitable forecasting strategy uses deep learning with a Micro-clustering (MC) job that mixes unsupervised and supervised clustering tasks utilizingKmeans and Gaussian Support Vector Machine. To guarantee accuracy. B-bidirectional LSTMs can store feed-forward and future hidden-layer data. Feedback and feed-forward loops do this. The DaviesBouldering index determined cluster production per hour. MC with B-LSTM networks improves prediction,especially around spike locations. Forecasting RE generation and grid load is difficult. Prosumer microgrids (PMGs) sell electricity to aggregators. A hybrid machine learning-based load and weather data transmission method provides the biggest benefit. ANFIS, MLP, and radial basis function artificial neural networks (ANNs) would be used in this technique (RBF). Machine learning-based hybrid forecasting can improve accuracy.

An Improved Self-Organizing Migration Algorithm for Short-Term Load Forecasting with LSTM Structure Optimization

Establishing an accurate and robust short-term load forecasting (STLF) model for a power system in safe operation and rational dispatching is both required and beneficial. Although deep long short-term memory (LSTM) networks have been widely used in load forecasting applications, it still has some problems to optimize, such as unstable network performance and long optimization time. This study proposes an adaptive step size self-organizing migration algorithm (AS-SOMA) to improve the predictive performance of LSTM. First, an optimization model for LSTM prediction is developed, which divides the LSTM structure seeking into two stages. One is the optimization of the number of hidden layer layers, and the other optimizes the number of neurons, time step, learning rate, epochs, and batch size. Then, a logistic chaotic mapping and an adaptive step size method were proposed to overcome slow convergence problems and stacking into local optimum of SOMA. Comparison experiments with SOMA, PSO, CPSO, LSOMA, and OSMA on test function sets show the advantages of the improved algorithm. Finally, the AS-SOMA-LSTM network prediction model is used to solve the STLF problem to verify the effectiveness of the proposed algorithm. Simulation experiments show that the AS-SOMA exhibits higher accuracy and convergence speed on the standard test function set and has strong prediction ability in STLF application with LSTM.

Robust Wavelet Transform Neural-Network-Based Short-Term Load Forecasting for Power Distribution Networks

A precise short-term load-forecasting model is vital for energy companies to create accurate supply plans to reduce carbon dioxide production, causing our lives to be more environmentally friendly. A variety of high-voltage-level load-forecasting approaches, such as linear regression (LR), autoregressive integrated moving average (ARIMA), and artificial neural network (ANN) models, have been proposed in recent decades. However, unlike load forecasting in high-voltage transmission systems, load forecasting at the distribution network level is more challenging since distribution networks are more variable and nonstationary. Moreover, existing load-forecasting models only consider the features of the time domain, while the demand load is highly correlated to the frequency-domain information. This paper introduces a robust wavelet transform neural network load-forecasting model. The proposed model utilizes both time- and frequency-domain information to improve the model’s prediction accuracy. Firstly, three wavelet transform methods, variational mode decomposition (VMD), empirical mode decomposition (EMD), and empirical wavelet transformation (EWT), were introduced to transform the time-domain demand load data into frequency-domain data. Then, neural network models were trained to predict all components simultaneously. Finally, all the predicted data were aggregated to form the predicted demand load. Three cases were simulated in the case study stage to evaluate the prediction accuracy under different layer numbers, weather information, and neural network types. The simulation results showed that the proposed robust time–frequency load-forecasting model performed better than the traditional time-domain forecasting models based on the comparison of the performance metrics, including the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE).

Cooperative ensemble learning model improves electric short-term load forecasting

Efficient models for short-term load forecasting (STLF) plays a crucial role in establishing the companies’ energetic planning due to their importance in electric power distribution and generation systems. An ensemble learning model based on dual decomposition approach, which combines two signals decomposition techniques, machine learning models and hyperparameters optimization based on metaheuristics, is applied to electric STLF. The seasonal and trend decomposition using locally-weighted regression (STL) decompose the time series into seasonal, trend, and residual components. Moreover, Variational Mode Decomposition (VMD) is applied to decompose the STL residual into different frequencies. Also, seasonal Naïve is adopted to handle the seasonal patterns. Moreover, due to the nonlinearities of the remaining components, an eXtreme gradient boosting model with hyperparameters tuned by a coyote optimization algorithm, extreme learning machines, ridge regression, and support vector regression models are employed to handle the STL trend and VMD components. The datasets from five regions and four seasons of the year for the Australian energy market operator are used to test the effectiveness of the proposed model for STLF with a multi-step-ahead horizon (12-hours-ahead and 24-hours-ahead). The performance analysis is based on the mean absolute error, symmetric mean absolute percentage error (sMAPE), overall weighted average, and Diebold–Mariano statistical test. The results of the study were divided into four comparative experiments: comparisons with (i) single decomposed models, (i) dual decomposed models, (iii) non-decomposed models, and (iv) state-of-art models. Regarding the sMAPE performance criterion, the proposed models achieved errors between 0.75%–3.18% and 1.56%–7.72% for STLF 12 and 24-hours-ahead. In the comparative scenarios, the proposed model improved the forecasting results up to 99% regarding compared models in terms of overall weighted average. Lastly, the proposed cooperative ensemble learning model outperformed 71.25% of the state-of-art models regarding forecasting error reduction in terms of sMAPE. From a broader perspective, the proposed cooperative ensemble learning model showed to be an efficient tool based of forecasting erros reduction for STLF due to its capability to improve forecasting accuracy and achieve reliable forecasting results compared with observed ones.

Short-term load forecasting based on sample weights assignment

Short-term load forecasting (STLF) is the basis of the power system operation. Considering that the importance of different training samples is different, a sample weights assignment method is proposed in this paper to help the STLF to learn the key sample. At first, the sample similarity is measured considering the characteristics of different input components. Based on this, training samples are selected. Finally, different training samples are assigned with different sample weights through the designed weights assignment function. With the proposed method, the STLF model is able to focus on the crucial samples. Simulation results considering different data-driven models demonstrate the effectiveness of the proposed method.

Intelligent wild geese algorithm with deep learning driven short term load forecasting for sustainable energy management in microgrids

Energy management in power grids becomes essential to reduce the cost for the consumer and improve the power supply reliability. The microgrid is a vital part of the smart grid and it requires intelligent power management approach for effective functioning. Presently, delivering demand load and sustaining energy are two major challenges that exist in the power system. To resolve these problems, short-term load forecasting (STLF) models have been presented as an effective management and energy supply mode in power systems. The recently developed deep learning (DL) and machine learning (ML) models can be employed for accurate STLF in microgrids. In this view, this study presents an intelligent wild geese algorithm with deep learning driven short term load forecasting (IWGADL-STLF) model for sustainable energy management in microgrids. The proposed IWGADL-STLF model intends to accurately and rapidly predict the STLF in the microgrids. To accomplish this, the IWGADL-STLF model uses attention based Bi-directional long short term memory (ABiLSTM) model which involves the input parameters as formation of household and commercial load profiles with commercial load profile of the microgrid as output. The proposed IWGADL-STLF model identifies the behavioural patterns of parameters and models the behaviour in short time period for effective prediction process. Since hyperparameters play a vital role in the DL models, in this study, WGA is applied as a hyperparameter optimizer of the ABiLSTM model. The IWGADL-STLF approach has shown effective results with low MAE, MAPE, and R2 values. A comprehensive experimental analysis reported the enhanced performance of the presented model over the other existing approaches under several aspects.

SHORT-TERM LOAD FORECASTING BY USING THE ARTIFICIAL NEURAL NETWORK MODEL

The article describes a model for short-term load forecasting by using the artificial neural network, and its application to load forecasting for a concrete distribution area. The artificial neural networks are mostly used in solving the problems of classification and prediction when the relations between input and output variables are highly complex and hard to describe exactly. Considering the stochastic nature and the major impact of weather conditions (temperature, humidity, wind, etc.) on electricity consumption, the application of artificial neural networks is suitable for short-term forecasting the load of an electric power system (EPS). Furthermore, the article describes the used models of artificial neural networks for seasonal and multiple daily load forecasts and presents the load forecast results for a distribution area supplied over the busbars of the 110/x substation at the HEP Elekroslavonija Distribution System Operator (HEP ODS).