Short-term Power Load Research Articles

Research on short-term load forecasting based on feature similarity using PSO algorithm

In this paper, a short-term power load forecasting model based on feature similarity was proposed. The model can comprehensively consider the short-term load forecasting under the condition of multiple factors, such as meteorological information, holiday/workday information and other factors in a unified framework. Different values of the same characteristic have great influence on load forecasting. Therefore, hierarchical clustering algorithm is used to analyse the value of each feature dimension. Feature distance is used as feature mapping value to establish feature mapping relation table. Because different factors have different weights for load forecasting, a weighted feature similarity measurement strategy is designed. Taking the minimum residual sum of squares as the optimization objective, particle swarm optimization algorithm is used to solve the optimal characteristic weight. The validity of the model and the accuracy of load forecasting are verified by comparing the numerical simulation with the existing models.

Short-term Power Load Forecast of an Electrically Heated House in St. John’s, Newfoundland, Canada

A highly efficient deep learning method for short-term power load forecasting has been developed recently. It is a challenge to improve forecasting accuracy, as power consumption data at the individual household level is erratic for variable weather conditions and random human behaviour. In this paper, a robust short-term power load forecasting method is developed based on a Bidirectional long short-term memory (Bi-LSTM) and long short-term memory (LSTM) neural network with stationary wavelet transform (SWT). The actual power load data is classified according to seasonal power usage behaviour. For each load classification, short-term power load forecasting is performed using the developed method. A set of lagged power load data vectors is generated from the historical power load data, and SWT decomposes the vectors into sub-components. A Bi-LSTM neural network layer extracts features from the sub-components, and an LSTM layer is used to forecast the power load from each extracted feature. A dropout layer with fixed probability is added after the Bi-LSTM and LSTM layers to bolster the forecasting accuracy. In order to evaluate the accuracy of the proposed model, it is compared against other developed short-term load forecasting models which are subjected to two seasonal load classifications.

Principal Component Analysis of Short-term Electric Load Forecast Data Based on Grey Forecast

With the large-scale development of the power industry, new requirements are put forward for the stable operation of the power system. Power system load forecasting refers to the use of historical load data to predict the future load value, which is an important part of energy management system. The short-term load forecasting process is often combined with the basic mechanism of power grid dispatching to achieve the balance of power grid supply and demand, reflecting the highly nonlinear computing ability. Power load forecasting is the premise of power grid real-time control, operation planning and development planning. At present, the grey model model used for power system load forecasting generally has the problems of large calculation amount, no mature theoretical basis for selecting structures and parameters, etc. This paper discusses the application of grey model in short-term power load forecasting, and puts forward a principal component analysis method suitable for ordinary daily power load forecasting data, which improves the accuracy of short-term power load forecasting.

A Hybrid Double Forecasting System of Short Term Power Load Based on Swarm Intelligence and Nonlinear Integration Mechanism

Accurate and reliable power load forecasting not only takes an important place in management and steady running of smart grid, but also has environmental benefits and economic dividends. Accurate load point forecasting can provide a guarantee for the daily operation of the power grid, and effective interval forecasting can further quantify the uncertainty of power load on this basis to provide dependable and precise load information. However, most of the previous work focuses on the deterministic point prediction of power load and rarely considers the interval prediction of power load, which makes the prediction of power load not comprehensive. In this study, a new double hybrid load forecasting system including point forecasting module and interval forecasting module is developed, which can make up for the shortcomings of incomplete analysis for the existing research. The point forecasting module adopts a nonlinear integration mechanism based on Back Propagation (BP) network optimized by Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to improve the accuracy of point prediction. A fuzzy clustering interval prediction method based on different data feature classification is successfully proposed which provides an effective tool for load uncertainty analysis. The experiment results show that the system not only has a good effect in accurately predicting power load, but also can analyze the uncertainty of the power load, which can be used as an effective technology of power system planning.

Power Load Forecast Based on Fuzzy BP Neural Networks with Dynamical Estimation of Weights

The short-term power load forecast is deeply studied by integrating fuzzy BP neural networks and composite adaptive filtering in this paper. Due to the difficulty on accurate modeling of complex factors in smart grid, the fuzzy technology is introduced to deal with the uncertain factors. Meanwhile, data-driven method, such as neural networks, is used as the basic frame of the power load forecast by combing the fuzzy technology. Thereby, to design a highly effective training scheme becomes the main problem on the weights of the BP-NNs. To realize better weights training, the combined filtering technology is fully adopted by fusing adaptive filtering methods based on observable degree (OD) analysis, Sage–Husa adaptive technology, and variational Bayesian method. Thereby, a novel power load forecast algorithm is proposed based on the fuzzy BP-NNs and the combined adaptive cubature Kalman filter. Experiment based on practical power load data is presented to validate the effectiveness of the proposed short-term power load algorithm.

Power Load Forecasting Using BiLSTM-Attention

With the development of big data and artificial intelligence, the applications of smart grid have received extensive attention. Specifically, accurate power system load forecasting plays an important role in the safety and stability of the power system production scheduling process. Due to the limitations of traditional load forecasting methods in dealing with large scale nonlinear time series data, in this paper, we proposed an Attention-BiLSTM (Attention based Bidirectional Long Short-Term Memory, Attention-BiLSTM) network to do the accurate short-term power load forecasting. This model is based on BiLSTM recurrent neural network which has high robustness on time series data modeling, and the Attention mechanism which can highlight the key features playing key roles in load forecasting in input data. The verification experiments with real data in a certain area show that the proposed model outperforms other models in terms of prediction accuracy and algorithm robustness.

Short-term Load Forecasting Model Based on IBFO-BILSTM

Short-term power load forecasting has always been a very important position in power grid operation. Improving prediction accuracy has great significance for improving the power company’s dispatch efficiency and economic benefits. In view of the feed-forward neural network cannot learn the time series relationship between load data, a hybrid depth model based on improved bacterial foraging algorithm (IBFO) and long short-term memory (LSTM) network is proposed for short-term power load forecasting. Using historical load data and its associated temperature data and date information as input, the initial weights and bias are optimized by improved bacterial foraging algorithm, and the multi-layer bidirectional LSTM neural network is used to mine the hidden time series relationship between input data, and finally output prediction. The proposed prediction model was verified by using the historical load data of Nantong. The data shows that the proposed model has higher prediction accuracy than other models.

Empirical Mode Decomposition based Multi-objective Deep Belief Network for short-term power load forecasting

With the rapid development of power grid data, the data generated by the operation of the power system is increasingly complex, and the amount of data increases exponentially. In order to fully exploit and utilize the deep relationship between data to achieve accurate prediction of power load, this paper proposes an Empirical Mode Decomposition Based Multi-objective Deep Belief Network prediction method (EMD-MODBN). In the training process of DBN, a multi-objective optimization model is constructed aiming at accuracy and diversity, and MOEA/D is used to optimize the parameters of the model to enhance the generalization ability of the prediction model. Finally, the final load forecasting results are obtained by summing up the weighted outputs of each forecasting model with ensemble learning method. The experimental results show that compared with several current better load forecasting methods, this method has obvious advantages in prediction accuracy and generalization ability.

Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach

Power load fluctuation is generally agreed to be a non-stationary stochastic process. The Fractional Brownian Motion (FBM) model is proposed to forecast a non-stationary time series with high accuracy. Computation of the Hurst exponent (H) for the power load data series using the Rescaled Range Analysis (R/S) in this study. This method is used to verify the Long-Range Dependent (LRD) characteristics of non-stationary power load data. For the real power load, however, H exponent takes on the self-similarity characteristics in a certain finite range of intervals, the global self-similarity is very rare to exist. The H exponent of the self-similarity usually has more than one value. We generalize multifractional H(t) to replace constant H. To improve the forecasting accuracy, the H(t) is optimized by the Quantum-Behaved Particle Swarm Optimization (QPSO). Once the optimal H(t) is obtained, then the optimal and parameters in the multi-Fractional Brownian Motion (mFBM) model can be deduced to forecast next power load data series with a higher accuracy.

Short-term power load forecasting using integrated methods based on long short-term memory

The development of power system informatization, the massive access of distributed power supply and electric vehicles have increased the complexity of power consumption in the distribution network, which puts forward higher requirements for the accuracy and stability of load forecasting. In this paper, an integrated network architecture which consists of the self-organized mapping, chaotic time series, intelligent optimization algorithm and long short-term memory (LSTM) is proposed to extend the load forecasting length, decrease artificial debugging, and improve the prediction precision for the short-term power load forecasting. Compared with LSTM prediction, the algorithm in this paper improves the prediction accuracy by 61.87% in terms of root mean square error (RMSE), and reduces the prediction error by 50% in the 40-fold forecast window under some circumstances.

A Hybrid Forecasting Model for Short-Term Power Load Based on Sample Entropy, Two-Phase Decomposition and Whale Algorithm Optimized Support Vector Regression

To improve the accuracy and reliability of short-term power load forecasting and reduce the difficulty caused by load volatility and non-linearity, a hybrid forecasting model (CEEMDAN-SE-VMD-PSR-WOA-SVR) is proposed. Firstly, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is employed to generate multiple intrinsic modal functions (IMF) by decomposing the historical power load series. Then the sample entropy (SE) of each IMF is calculated to quantitatively evaluate the corresponding complexity. Afterward, variational mode decomposition (VMD) is adopted to achieve secondary decomposition for the component with the maximum sample entropy. Subsequently, the phase space reconstruction (PSR) is applied to reconstruct each IMF into a high-dimensional feature space matrix, which is formed as the input of support vector regression (SVR). Finally, SVR optimized by whale optimization algorithm (WOA) is used for the prediction, where the predicted values of all IMFs are accumulated to obtain the final prediction results. The experimental result demonstrates that the proposed hybrid model can effectively decompose the load series with non-linear characteristic and provide more accurate forecasting results by comparing the other models.

Short-Term Power Load Forecasting Based on Cross Multi-Model and Second Decision Mechanism

Short-term load forecasting (STLF) plays a vital role in the reliable, secure, and efficient operation of power systems. Since electric load variation results from diverse factors, accurate and stable load forecasting remains a challenging task. To increase the forecasting accuracy and stability, in this paper, we newly propose a short-term load forecasting method based on the cross multi-model and second decision mechanism. First, we combine horizontal and longitudinal training set selection method to construct the cross training sets, which acquire both the horizontal and longitudinal characteristics of the load variation. Second, to improve the generalization ability and extend the application scope, we construct forecasting multi-models by training multiple forecasting algorithms with cross training sets. Finally, to aggregate the forecasting outputs obtained by the forecasting multi-models, we propose a second decision mechanism based on a decision multi-model and adaptive weight allocation strategy, which overcomes the limited learning ability shortcoming of single decision models and further improves the forecasting accuracy. Case studies based on electrical load data from the state of Maine, the region of New England, Singapore, and New South Wales of Australia show that both the accuracy and the stability of the proposed method are superior to the compared models.

Research on Short-term Power Load Forecasting Method Based on Temperature Accumulation Effect Embedded in Time Series

Short-term power load forecasting is an important link in power grid dispatching, which has an important impact on unit combination, economic dispatching, optimal power flow, etc. As meteorological factors have great influence on load, the influence of meteorological factors should be considered reasonably in short-term load forecasting. The accuracy of short-term electric charge prediction results can help senior personnel of the power system to make accurate and feasible power operation methods. In order to ensure the safe and stable operation of power grid in various special periods and to ensure the economic benefits of related power enterprises to the greatest extent, it is imperative to establish a highly accurate prediction model. The accuracy of power load forecasting will directly affect the position of each power enterprise in the market. Based on the function of time series embedding, this paper analyzes the rule of cumulative effect on load. The correlation between temperature and load is greatly improved after considering the cumulative effect to deal with temperature correction.

An Adaptive and Parallel Forecasting Strategy for Short-Term Power Load Based on Second Learning of Error Trend

Modeling an accurate forecasting model for short-term load is still challenging due to the diverse causes of load changing and lack of information on many of these causes. In this paper, error trend is used to reveal the trend effect caused by unknown load affecting factors and proposed adaptive second learning of error trend (A-SLET) to self-adapt the trend effect. Furthermore, the training set is classified based on balance point temperature and then parallelly trained and tested adaptive forecaster for hot days and adaptive forecaster for cold days with proper data. Combining A-SLET with parallel forecasting and training set classification, Adaptive and Parallel forecasting strategy based on Second Learning of Error Trend (AP-SLET) is proposed. The work studied two distinct load patterns, one in the USA and the other in Australia. Considering the yearly forecasting horizon, MAPE of the adaptive and parallel forecasting strategy is 1.87%-4.04% for ME-Maine of New England and 2.81%-4.41% for New South Wales. Compared to the state-of-art forecasting methods, MAPE of the adaptive and parallel forecasting strategy is reduced by 17.03%-33.33%, RMSE and MAE are reduced by 34.05% and 35.38% respectively. The experimental results demonstrate the proposed strategy can transform unknown and unavailable load affecting factors into known forecasting features and then adapt it to improve forecasting performance. The proposed strategy is also forecaster independent and equally applicable to almost all load scenarios regardless of geographical and seasonal differences.

Short-term power load forecasting of GWO-KELM based on Kalman filter

Short-term power load forecasting plays a significant role in power system security management. The prediction model in this paper is the grey wolf optimization algorithm to optimize kernel extreme learning machine (GWO-KELM). First, the Kalman filter is used to reduce the noise for the random noise interference existing in the power load data. Then determine the input and output of the prediction model. In this paper, the ELM model of three different kernel functions is used for comparative experiments, and the mean absolute percentage error is used as the evaluation model index. It is concluded from the experimental results that the GWO-KELM model used in this article has the advantages of high prediction accuracy and strong generalization ability, so it is practicable to apply the model to short-term electric load forecasting.

Short Term Prediction of Photovoltaic Power Based on FCM and CG-DBN Combination

Affected by many factors, the photovoltaic output power is characterized by nonlinearity, volatility and instability. Therefore, short-term forecasting models are required to have multiple inputs, levels, and categories. In order to solve the above problems and improve the accuracy of predictions, this paper proposes a combined model prediction method based on similar-day clustering and the use of Conjugate Gradient (CG) to improve Deep Belief Network (DBN). The initial method uses fuzzy C-Means Clustering Algorithm (FCM) to perform similar-day clustering on the original data according to the degree of membership. The CG-DBN prediction model is then designed according to the category, with the model ultimately being used to perform the short-term prediction of the PV output power. The proposed scheme uses data from Zhejiang Longyou power station for experimental analysis and verification, and the results were compared with the back propagation neural networks model, Support Vector Machine (SVM) model, and traditional deep belief network. The model’s predicted results are compared. Finally, it is concluded that, in the short-term PV power load forecasting, the prediction performance of the FCM and CG-DBN combination forecast model is better than the above three models and has strong feasibility in short-term PV power forecasting.

Application of residual self-fitting ensemble neural network based on LSTM in short-term power load forecasting

Accurate load forecasting helps to improve the safety and stability of power systems. LSTM neural networks based on deep learning has lower predicted error than traditional machine learning and time series models. After calculating the residuals of the predicted values and observed values of the LSTM and traditional machine learning model, it can be found that the distribution of residuals also possessed with the regularity of time series data. Therefore, the residual time series can be fitted by another model based on this regularity. To avoid the limitations of a single model, a variety of neural network models have been used to compose ensemble model. In this model, the residual can be generated and fitted by itself. In other words, it implements self-fitting. After analysing the residual distribution and the ensemble method of predictive model, this paper presents a new ensemble neural network model used for short-term power load forecasting. After the experiment, it’s obvious that the predicted results of the new ensemble neural networks is more accurate than single LSTM.

A new optimization algorithm for non-stationary time series prediction based on recurrent neural networks

Deep neural network with recurrent structure was proposed in the recent years and has been applied to time series forecasting. Many optimization algorithms are developed under the assumption of invariant and stationary data distributions, which is invalid for the non-stationary data. A novel optimization algorithm for modeling non-stationary time series is proposed in this paper. A moving window and exponential decay weights are used in this algorithm to eliminate the effects of the history gradients. The regret bound of the new algorithm is analyzed to ensure the convergency of the calculation. Simulations are done on short-term power load data sets, which are typically non-stationary. The results are superior to the existing optimization algorithms.

Short‐term power load forecasting based on multi‐layer bidirectional recurrent neural network

Accurate power load forecasting is of great significance to ensure the safety, stability, and economic operation of the power system. In particular, short-term power load forecasting is the basis for grid planning and decision making. In recent years, machine learning algorithms have been widely used for short-term power load forecasting. Specifically, long short-term memory (LSTM) and gated recurrent unit (GRU) are tailored to time series data. In this study, a multi-layer bidirectional recurrent neural network model based on LSTM and GRU is proposed to forecast short-term power load and is validated on two data sets. The experimental result shows that the proposed method is superior to the competition winner in the precision of forecasting on the European Intelligent Technology Network competition data. On power company data in Chongqing, considering the differences of the seasonal load, the hourly peak load of different types of load data is used for experiments. The authors separately forecast the seasonal load and compare it with LSTM, support vector regression and back propagation models. The results of the comparison show the priority of the proposed method in terms of forecasting accuracy as compared to the adopted models.

Short-term power load forecasting based on Elman neural network with particle swarm optimization

The prediction of short term power load owns a great influence on performance of the whole electric system. Raising the result of power load forecasting is always a research spot. This paper proposes a method combined Elman neural network (ENN) and the particle swarm optimization (PSO) for the short-term power load forecasting. Firstly, this paper introduces the principle of ENN and PSO algorithm respectively and analyzes performance of network influenced by parameters of ENN. Then the particle swarm optimization algorithm is applied to searching the optimal learning rate of ENN. To study the capability of the method proposed in this paper, the method is utilized for the short-term power load forecasting, which is suitable to be solved by ENN. Besides, a comparison experiment on this method (PSO–ENN) with general regression neural network (GRNN), the original ENN and the traditional back-propagation neural network (BPNN) is given out to illustrate effectiveness of PSO–ENN.