Research on Load Forecasting Method Considering Data Feature Analysis Based on Bi-LSTM Network

To better mine the effective information contained in massive data and improve the accuracy of short-term load forecasting, this paper proposes a hybrid model based on convolutional neural network and long short-term memory network (CNN-LSTM) for short-term load forecasting (STLF), which takes massive historical load data, meteorological data, date information, and peak-valley electric price data as input by constructing continuous feature maps with time sliding window. Convolutional neural network (CNN) is used to extract features and reshape them into vectors. The feature vectors are constructed in temporal and fed into long short-term memory network (LSTM) which is used to predict STLF. To the best of our knowledge, this is the first work to predict STLF using deep learning in both spatial and temporal domains. It is shown that the forecasting accuracy can be notably improved by CNN-LSTM hybrid model method. The effectiveness of the proposed method is validated through extensive comparison studies on a real-world dataset.

The accurate prediction of power system load is extremely important for the operation of the power market and the safe operation of the power grid. In order to improve the accuracy of short-term load forecasting of power systems, a combination model based on long short term memory network (LSTM) and light gradient boosting machine (LightGBM) is proposed. The experiment first decomposes historical load data by EMD, uses historical weather data and load data decomposed by EMD to establish LSTM prediction model and LightGBM prediction model respectively, and then these two predicted values are linearly combined to obtain the final predicted value. The electrical load data of the 2016 Electrician Mathematical Contest in Modeling is used as an example to verify. The experimental results show that the LSTM-LightGBM combined model has higher forecasting accuracy and application prospects for power load forecasting than traditional load forecasting methods and standard LSTM and LightGBM load forecasting methods.

Short-term load forecasting is very important for power system dispatching since the economy and stability of power system are affected by the accuracy of load forecasting. Therefore, a short-term load forecasting method based on deep belief neural network (DBN) is proposed in this paper. Firstly, the low-voltage user load data are normalized to construct the training set and test set for low-voltage user short-term load forecasting; then, the DBN model is trained and adjusted by using the training set to obtain the well-trained DBN network; finally, the historical data of short-term load of users are input for load forecasting. The actual load data in Zhejiang power grid of China are serviced for demonstrating the effectiveness the proposed algorithm. The simulation results show that the short-term load forecasting algorithm proposed in this paper is with high accuracy for the actual load forecasting of low voltage users.

A short-term load forecasting model based on mixup and transfer learning

Load forecasting has always been the focus of energy management system research. Recently, with the development of machine learning and artificial intelligence technology, more and more models are applied to load forecasting. In this paper, we design a model based on the temporal convolutional network for short-term load forecasting, which can accurately capture the feature form historical load data. Combine the actual load data collected from a certain region of Shanghai, we compare our model with three traditional models, including ARIMA model, ANN model, and LSTM model. The experiment results show that the model proposed in this paper achieves the best performance and has superior accuracy in short-term load forecasting.

Accurate load forecasting is essential for reliable and efficient operation of power systems. Traditional forecasting methods often struggle with capturing complex nonlinear patterns in load data. Artificial neural networks (ANNs) have emerged as a promising alternative due to their ability to learn complex relationships from historical data (Syed et al. in IEEEA 9:54992–55008, 2021. https://doi.org/10.1109/ACCESS.2021.3071654). This study investigates the potential of ANNs for short-term peak load forecasting in a 150 kV power system in Semarang, Indonesia. The study examines the impact of different input variables, including historical peak load, minimum load, population, and energy production, on forecasting accuracy. Several ANN architectures are trained and evaluated using mean absolute percentage error (MAPE) and mean squared error (MSE) metrics as reported by Demuth and De Jesús (neural network design). The results indicate that ANNs can achieve high accuracy in predicting peak load, with MAPE values below 10%. The study also demonstrates the importance of carefully selecting input variables and training parameters for optimal model performance. The findings highlight the potential of ANNs for improving load forecasting accuracy in power systems, contributing to enhanced grid reliability and operational efficiency. The findings of this study contribute to a deeper understanding of the application of ANNs in power system load forecasting. They demonstrate the potential of ANNs to achieve high accuracy and provide valuable insights into the factors influencing model performance. The findings are relevant for power system operators, researchers, and policymakers working to improve grid reliability and efficiency as reported by Prabha Kundur and Malik (Power System Stability and Control, McGraw-Hill Education, New York, 2022. https://www.accessengineeringlibrary.com/content/book/9781260473544).

Smart distribution network gathers massive load data with SCADA system. Bus load forecasting needs to increase the process speed while assuring the forecasting accuracy, to meet the real-time requirements. This paper proposed a fast bus load forecasting method for smart distribution system. This method utilizes the historical load and temperature data to establish a forecasting model, which derived the reference load data of the forecasting day. With temperature data of the forecasting day and the historical data, it is deduced the load correction of the forecasting day to realize online load forecasting, which increases the forecasting speed. By the instance analysis, the speediness and accuracy of the proposed method is verified.

Day-ahead load and wind power forecasts provide useful information for operational decision making, but they are imperfect and forecast errors must be offset with operational reserves and balancing of (real time) energy. Procurement of these reserves is of great operational and financial importance in integrating large-scale wind power. We present a probabilistic method to determine net load forecast uncertainty for day-ahead wind and load forecasts. Our analysis uses data from two different electric grids in the US with similar levels of installed wind capacity but with large differences in wind and load forecast accuracy, due to geographic characteristics. We demonstrate that the day-ahead capacity requirements can be computed based on forecasts of wind and load. For 95% day-ahead reliability, this required capacity ranges from 2100 to 5700 MW for ERCOT, and 1900 to 4500 MW for MISO (with 10 GW of installed wind capacity), depending on the wind and load forecast values. We also show that for each MW of additional wind power capacity for ERCOT, 0.16–0.30 MW of dispatchable capacity will be used to compensate for wind uncertainty based on day-ahead forecasts. For MISO (with its more accurate forecasts), the requirement is 0.07–0.13 MW of dispatchable capacity for each MW of additional wind capacity.

The electricity load forecasting handles the process of determining how much electricity will be available at a given time while maintaining the balance and stability of the power grid. The accuracy of electricity load forecasting plays an important role in ensuring safe operation and improving the reliability of power systems and is a key component in the operational planning and efficient market. For many years, a conventional method has been used by using historical data as input parameters. With swift progress and improvement in technology, which shows more potential due to its accuracy, different methods can be applied depending on the identified model. To enhance the forecast of load, this paper introduces and proposes a framework developed on graph database technology to archive large amounts of data, which collects measured data from electrical substations in Pristina, Kosovo. The data includes electrical and weather parameters collected over a four-year timeframe. The proposed framework is designed to handle short-term load forecasting. Machine learning Linear Regression and deep learning Long Short-Term Memory algorithms are applied to multiple datasets and mean absolute error and root mean square error are calculated. The results show the promising performance and effectiveness of the proposed model, with high accuracy in load forecasting.

Improving the accuracy of power system load forecasting is important for economic dispatch. However, a load sequence is highly nonstationary and hence makes accurate forecasting difficult. In this paper, a method based on wavelet decomposition (WD) and a second-order gray neural network combined with an augmented Dickey–Fuller (ADF) test is proposed to improve the accuracy of load forecasting. First, the load sequence is decomposed by WD to reduce the nonstationary load sequence. Then, the ADF test is adopted as the test method for the stationary load sequence of each decomposed component after WD in which the test results determine the best WD level. Finally, because forecasting the wavelet details characterized by high frequencies is difficult owing to its fluctuation, a second-order gray forecasting model is used to forecast each component after WD. Furthermore, to obtain the optimum parameters of the second-order gray forecasting model, the neural network mapping approach is used to build the second-order gray neural network forecasting model. The simulation result of a real load sequence verifies that the method proposed in this paper can effectively improve the load-forecasting accuracy.

Short-term load forecasting plays a vital role for the power system’s safe operation and production arrangements. This paper regards electric load forecasting as a nonlinear time series prediction problem, and establishes the electric load autoregressive prediction model (ARX Model) based on historical load data, approaching ARX model parameters with RBF neural network, estimating model parameters with using a Structured Nonlinear Parameters Optimization Method (SNPOM), proposing cycle prediction method for short-term electric load forecasting. Experimental results show that the method has higher prediction accuracy for short-term electric load forecasting. Keywords: Short-term load forecasting; time series; RBF-ARX model; cycle forecasting; structured nonlinear parameter optimization method

In electrical engineering load forecasting have been tried out using traditional forecasting models and artificial intelligence techniques and have become one of the major research fields. An accurate and efficient Short-term load forecasting (STLF) plays a vital role for economic operational planning of both regulated power systems and electricity markets. To develop a solution/methodology to demand forecast (Hourly load forecast) and by incorporating weather conditions. However, STLF it is pertinent to understand conventional methods. Therefore, popular conventional methods were implemented to learn methods for STLF. This paper presents Simple Moving Average, Weighted Moving Average, Exponential Moving Average, Auto Regressive and Multiple linear regressions for short term load forecasting. Conventional technique approach is implemented on historical load data for forecasting the load. PGVCL hourly load data used for training and testing is collected from ALDC, Jetpur, Gujarat. Hence hereby in this paper I have compared five conventional methods for STLF and have come out with a result that forecasting errors of time series models gives reasonably accurate hour ahead load forecast.

Bus load forecasting plays an important role in terms of the safety of power grid dispatching and operation and the accuracy of online analysis and decision-making. In order to improve accuracy of bus load forecasting, this paper proposes an ultra-short-term load forecasting method based on multi-type data and hybrid model. Under the background of power big data, the multi-type data such as historical bus load data, date information and weather information are firstly taken as the input features of the prediction model, which is established based on the fusion of back propagation (BP-ANN) neural network and convolutional neural network(CNN). BP-ANN is used to extract the feature vectors of numeric type and category type data, which is concatenated with feature vectors extracted by CNN from image type data. The combined features are fed to a fully connected multilayer Neural Network to predict the bus load. In this paper, the historical data of active power load at the high voltage side of a 220kV substation in a certain area of China and the corresponding weather information are used for example analysis. The analysis of experimental results shows that the proposed method can make full use of the characteristics of multi-source data to conduct ultra-short-term bus load prediction, and it has higher load prediction accuracy compared to the single model prediction of BP-ANN and CNN.

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.

Based on the features of both distributed artificial intelligence and load forecasting techniques, a distributed problem-solving system for short-term load forecasting is proposed. Such a distributed paradigm is a multi-agent system, each processing agent of which can compute autonomously and cooperate with other agents to reason an accurate and satisfactory solution for load forecasting. The designed load forecasting system solves problems using three basic moduli: blackboard module, knowledge sources, and control mechanism. In addition, to achieve a high degree of accuracy in load forecasting, the techniques are embedded in the domain knowledge source. The system was implemented by an expert system and tested. It was found that the developed distributed system is a valuable tool to system operators for short-term load forecasting. >