Monthly Load Forecasting Research Articles

Medium- and long-term power load forecasting model

Power load is an important part of power system, and power load forecasting has an important impact on power system analysis, design and control. With the development of smart micro grid, load forecasting has gradually become an important module in the energy management system, It is “source, network, load and storage” “An important link in energy flow matching. The staged combined demand forecasting model of power grid based on neural network and polynomial regression is adopted, and judgment conditions are added to the neural network. If the training sample data does not converge in the neural network training process, the neural network forecasting is terminated, and the data is automatically transferred to the polynomial regression model to obtain the forecasting results. This method can be initially used for annual and monthly load forecasting. It is an intelligent micro grid The planning of has laid a certain technical foundation.

Load forecasting based on deep neural network and historical data augmentation

Load forecasting is a complex non-linear problem with high volatility and uncertainty. This study presents a novel load forecasting method known as deep neural network and historical data augmentation (DNN–HDA). The method utilises HDA to enhance regression by DNN for monthly load forecasting, considering that the historical data to have a high correlation with the corresponding predicted data. To make the best use of the historical data, one year's historical data is combined with the basic features to construct the input vector for a predicted load. In this way, if there is C years' historical data, one predicted load can have C input vectors to create the same number of samples. DNN–HDA increases the number of training samples and enhances the generalisation of the model to reduce the forecasting error. The proposed method is tested on daily peak loads from 2006 to 2015 of Austria, Czech and Italy. Comparisons are made between the proposed method and several state-of-the-art models. DNN–HDA outperforms DNN by 44%, 38% and 63% on the three data sets, respectively.

Neutral Current Reduction in Three-Phase Four-Wire Distribution Feeders by Optimal Phase Arrangement Based on a Full-Scale Net Load Model Derived from the FTU Data

An increase in the neutral current results in a malfunction of the low energy over current (LCO) protective relay and raises the neutral-to-ground voltage in three-phase, four-wire radial distribution feeders. Thus, the key point for mitigating its effect is to keep the current under a specific level. The most common approach for reducing the neutral current caused by the inherent imbalance of distribution feeders is to rearrange the phase connection between the distribution transformers and the load tapped-off points by using the metaheuristics algorithms. However, the primary task is to obtain the effective load data for phase rearrangement; otherwise, the outcomes would not be worthy of practical application. In this paper, the effective load data can be received from the feeder terminal unit (FTU) installed along the feeder of Taipower. The net load data consisting of customers’ power consumption and the power generation of distributed energy resources (DERs) were measured and transmitted to the feeder dispatch control center (FDCC). This paper proposes a method of establishing the equivalent full-scale net load model based on FTU data format, and the long short-term memory (LSTM) was adopted for monthly load forecasting. Furthermore, the full-scale net load model was built by the monthly per hour load data. Next, the particle swarm optimization (PSO) algorithm was applied to rearrange the phase connection of the distribution transformers with the aim of minimizing the neutral current. The outcomes of this paper are helpful for the optimal setting of the limit current of the LCO relay and to avoid its malfunction. Furthermore, the proposed method can also improve the three-phase imbalance of distribution feeders, thus reducing extra power loss and increasing the operating efficiency of three-phase induction motors.

Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory

Accurate load forecasting can help alleviate the impact of renewable-energy access to the network, facilitate the power plants to arrange unit maintenance and encourage the power broker companies to develop a reasonable quotation plan. However, the traditional prediction methods are insufficient for the analysis of load sequence fluctuations. The economic variables are not introduced into the input variable selection and the redundant information interferes with the final prediction results. In this paper, a set of the ensemble empirical mode is used to decompose the electricity consumption sequence. Appropriate economic variables are as selected as model input for each decomposition sequence to model separately according to its characteristics. Then the models are constructed by selecting the optimal parameters in the random forest. Finally, the result of the component prediction is reconstituted. Compared with random forest, support vector machine and seasonal naïve method, the example results show that the prediction accuracy of the model is better than that of the contrast models. The validity and feasibility of the method in the monthly load forecasting is verified.

Based on Partial Least Squares Linear Regression and the Improved Grey Prediction Model of Monthly Load Forecasting in Power System Applications

Aiming at monthly load of power system, it is forecasted by using the method of partial least squares regression and the model of improving grey prediction.First, using improved grey prediction model to forecast impact factors,then establishing partial least squares model according to the characteristics of the monthly load and the change of the main impact factors. The final fitted out a linear relation between load and impact factors. Practical example shows that the method has higher prediction accuracy, effectiveandfeasible.

An Innovative Application Method of Monthly Load Forecasting for Smart IEDs

This paper develops a new Intelligent Electronic Device (IED), and then presents an application method of a monthly load forecasting algorithm on the smart IEDs. A Multiple Linear Regression (MLR) model implemented with Recursive Least Square (RLS) estimation is established in the algorithm. Case Study proves the accuracy and reliability of this algorithm and demonstrates the practical meanings through designed screens. The application method shows the general way to make use of IED's smart characteristics and thereby reveals a broad prospect of smart function realization in application.

Monthly Load Forecasting Based on Optimum Grey Model

Due to the variety and the randomicity of its influencing factors, the monthly load forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on improved GM (1, 1).First, the GM (1, 1) is used to forecast the load data, which takes the longitude historical data as original series, the increment trend of load was forecasted and takes the crosswise historical data as original series, the fluctuation trend of load was forecasted. On this basis the optimum method is led in. An optimal integrated forecasting model is built up. The case calculation results show that the proposed method can remarkably improve the accuracy of monthly load forecasting, and decrease the error. The integrated model this paper describes for short-term load forecasting is available and accurate.