Holiday Load Research Articles

Holiday Peak Load Forecasting Using Grammatical Evolution-Based Fuzzy Regression Approach

Peak load forecasting plays an important role in electric utilities. However, the daily peak load forecasting problem, especially for holidays, is fuzzy and highly nonlinear. In order to address the nonlinearity and fuzziness of the holiday load behaviors, a grammatical evolution-based fuzzy regression approach is proposed in this paper. The proposed hybrid approach is based on the theorem that fuzzy polynomial regression can model all fuzzy functions. It employs the rules of the grammatical evolution to generate fuzzy nonlinear structures in polynomial form. Then, a two-stage fuzzy regression approach is used to determine the coefficients and calculate the fitness of the fuzzy functions. An artificial bee colony algorithm is used as the evolution system to update the elements of the grammatical evolution system. The process is repeated until a fuzzy model that best fits the load data is found. After that, the developed fuzzy nonlinear model is applied to forecast holiday peak load. Considering that different holidays possess different load patterns, a separate forecaster model is built for each holiday. Test results on real load data show that an averaged absolute percent error less than 2% can be achieved, which significantly outperforms existing methods involved in the comparison.

Holiday Load Power Forecasting Based on SOIF-PAMAM method

Focusing on the particularity of holiday load, in this paper, a periodic autoregressive moving average model (PAMAM) algorithm based on selecting optimal input features (SOIF) is proposed to predict the short-term holiday power load. In short-term load forecasting models, there are few researches on feature selection (FS). However, as more and more intelligent hybrid models are used in real-time load forecasting, FS has become a key factor affecting the forecasting accuracy. Based on the idea of SOIF, PAMAM model is proposed to improve the influence of FS factors, and the holiday equations are combined into periodic autoregressive moving average model, so as to improve the short-term forecasting. In order to simplify the calculation, in this paper, the probability distribution is used to calculate the FS, and the autoregressive spline algorithm is used to establish the nonlinear solar radiation and temperature effect model. Based on the statistics of solar radiation intensity, temperature and other data during the Spring Festival, in this paper we analyze the influence of the above factors on the short-term power load forecasting during holidays. Experimental results show that SOIF-PAMAM algorithm in which temperature and other weather conditions are considered can significantly improve the prediction accuracy, the average absolute error is 2.45%, and the root mean square error is 2.61%.

Optimized Seq2Seq model based on multiple methods for short-term power load forecasting

Accurate power load prediction plays a key role in reducing resource waste and ensuring stable and safe operations of power systems. To address the problems of poor stability and unsatisfactory prediction accuracy of existing prediction methods, in this paper, we propose a novel approach for short-term power load prediction by improving the sequence to sequence (Seq2Seq) model based on bidirectional long-short term memory (Bi-LSTM) network. Different from existing prediction models, we apply convolutional neural network, attention mechanism, and Bayesian optimization for the improvement of the Seq2Seq model. Moreover, in the data processing stage, we use the random forest algorithm for feature selection, and also adopt the weighted grey relational projection algorithm for holiday load processing to process the data and thereby overcome the difficulty of holiday load prediction. To validate our model, we choose the power load dataset in Singapore and Switzerland as experimental data and compare our prediction results with those by other models to show that our method can generate a higher prediction accuracy.

Study on the Operation Optimization Dispatching Strategy of Small and Medium-sized Pumped Storage Power Station

This paper uses equivalent substitution method and random production simulation method to calculate the static efficiency of daily operation of small and medium-sized pumped storage power stations, maximize the static efficiency under energy storage constraints, and obtain the daily output operation scheduling plan of pumped storage power station. Their utility and effectiveness are demonstrated by using multiple load curves including summer peak load and holiday load and applying the calculation results to practical scheduling operations. This method for the same type of pumped storage power station, has a general practical and guiding value.

Holiday load forecast based on combination of multi-scale features

Aiming at the difficulty of load forecasting due to the current holiday load jump, a holiday load forecasting method with multi-scale feature combination is proposed. Feature extraction and recoding of date information, load information and weather information, and effectively use of historical data information, reconstruct load forecast feature combination. This method is used to reconstruct the electric load data of a certain area in Jiangsu Province. XGBoost and LSTM were used to predict the holiday load in the reconstructed multi-scale feature combination dataset and the traditional feature combination dataset. The experimental results show that in both load forecasting models, this feature combination method can effectively mine the latent relationship contained in historical data, represent more refined prior knowledge, and improve the accuracy of holiday load forecasting.

An Extensible Framework for Short-Term Holiday Load Forecasting Combining Dynamic Time Warping and LSTM Network

Due to the extreme change of human behavior, the load consumption in public holidays fluctuates more significantly compared to general weekdays resulting in the difficulty of hourly holiday load forecasting. The holiday load forecasting is even challenging because the forecast is practically predicted on the nearest workday which might be more than one days prior to the public holiday. In this paper, we propose a Joint Dynamic time warping and LSTM (JDL) framework, to predict the hourly holiday load consumption on the nearest workday which is at least one day before the incoming holiday. The proposed JDL is a hybrid short-term holiday forecasting framework which combines dynamic time warping (DTW) and long-short term memory (LSTM) network. The DTW predicts the load consumption of the nearest workday and any preceding compensatory holiday(s), if any, based on the similar holiday occurrence pattern. LSTM predicts the highly unpredictable load consumption of the target holiday by univariate and multivariate models. Current results show the proposed JDL outperforms others.

A hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting for holiday load forecasting

In short-term load forecast (STLF), forecasting holiday load is one of the most challenging problems. Aimed at this problem, a hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting (XGBoost) is presented. It divides holiday STLF problem into the predictions for proportional curve and daily extremum of electricity demand, which are relatively independent and relate to different factors. It is benefit for holiday STLF by task decomposing. Based on the shape similarity measured by Euclidean distance, the proportional curve is predicted by pattern sequence-based matching method. Daily extremum of electricity demand is predicted by XGBoost considering holiday classification. Finally, the predicted holiday load profile is synthesized from the above two prediction results with segment correction. The proposed methodology can analyze holiday load characteristics more effectively and get a higher prediction accuracy independent of sufficient data and expert experience. We evaluate our methodology with many algorithms on a real data set of one provincial capital city in eastern China. The results of case studies show that the proposed methodology gives much better forecasting accuracy with an average error 2.98% in holidays.

A learning framework based on weighted knowledge transfer for holiday load forecasting

Since the variation pattern of load during holidays is different than that of non-holidays, forecasting holiday load is a challenging task. With a focus on this problem, we propose a learning framework based on weighted knowledge transfer for daily peak load forecasting during holidays. First, we select source cities which can provide extra hidden knowledge to improve the forecast accuracy of the load of the target city. Then, all the instances which are from source cities and the target city will be weighted and trained by the improved weighted transfer learning algorithm which is based on the TrAdaBoost algorithm and can decrease negative transfer. We evaluate our method with the classical support vector machine method and a method based on knowledge transfer on a real data set, which includes eleven cities from Guangdong province to illustrate the performance of the method. To solve the problem of limited historical holiday load data, we transfer the data from nearby cities based on the fact that nearby cities in Guangdong province have a similar economic development level and similar load variation pattern. The results of comparative experiments show that the forecasting framework proposed by this paper outperforms these methods in terms of mean absolute percent error and mean absolute scaled error.

Optimize Short Term load Forcasting Anomalous Based Feed Forward Backpropagation

This paper contains the Short-Term Load Forecasting (STLF) using artificial neural network especially feed forward back propagation algorithm which is particularly optimized in order to getting a reduced error value result. Electrical load forecasting target is a holiday that hasn’t identical pattern and different from weekday’s pattern, in other words the pattern of holiday load is an anomalous. Under these conditions, the level of forecasting accuracy will be decrease. Hence we need a method that capable to reducing error value in anomalous load forecasting. Learning process of algorithm is supervised or controlled, then some parameters are arranged before performing computation process. Momentum constant a value is set at 0.8 which serve as a reference because it has the greatest converge tendency. Learning rate selection is made up to 2 decimal digits. In addition, hidden layer and input component are tested in several variation of number also. The test result leads to the conclusion that the number of hidden layer impact on the forecasting accuracy and test duration determined by the number of iterations when performing input data until it reaches the maximum of a parameter value.

Holidays short-term load forecasting using fuzzy improved similar day method

SUMMARY Holidays load forecasting is one of the most challenging topics in the short-term load forecasting area. This is mainly due to different load behaviors and insufficient samples of holidays compared with the normal weekdays. This paper proposes an effective method for holidays short-term load forecasting that is robust to face with holidays of lunar year and simple for practical applications. The first step is to identify the essential characteristics of the holiday's profiles. For each characteristic, one similar day is selected. The essential load characteristics of the intended holiday are derived from these similar days. The holiday's load is forecasted by combining these characteristics and then will be modified using a fuzzy method. The proposed method has been implemented on Isfahan grid. Results show the proper performance of the proposed method especially for the holidays from the lunar year, which are varying compared with the solar year. Copyright © 2012 John Wiley & Sons, Ltd.

Holiday Load Forecasting Using Fuzzy Polynomial Regression With Weather Feature Selection and Adjustment

The load forecasting problem is a complex nonlinear problem linked with social considerations, economic factors, and weather variations. In particular, load forecasting for holidays is a challenging task as only a small number of historical data is available for holidays compared with what is available for normal weekdays and weekends. This paper presents a fuzzy polynomial regression method with data selection based on Mahalanobis distance incorporating a dominant weather feature for holiday load forecasting. Selection of past weekday data relevant to a given holiday is critical for improvement of the accuracy of holiday load forecasting. In the paper, a data selection process incorporating a dominant weather feature is also proposed in order to improve the accuracy of the fuzzy polynomial regression method. The dominant weather feature for selection of historical data is identified by evaluating mutual information between various weather features and loads from season to season. The results of case studies are presented to show the effectiveness of the proposed method.

The 24 Hourly Load Forecasting of the Election Day Using the Load Variation Rate

Short-term electric load forecasting of power systems is essential for the power system stability and the efficient power system operation. An accurate load forecasting scheme improves the power system security and saves some economic losses in power system operations. Due to scarcity of the historical same type of holiday load data, most big electric load forecasting errors occur on load forecasting for the holidays. The fuzzy linear regression model has showed good accuracy for the load forecasting of the holidays. However, it is not good enough to forecast the load of the election day. The concept of the load variation rate for the load forecasting of the election day is introduced. The proposed algorithm shows its good accuracy in that the average percentage error for the short-term 24 hourly loads forecasting of the election days is 2.27%. The accuracy of the proposed 24 hourly loads forecasting of the election days is compared with the fuzzy linear regression method. The proposed method gives much better forecasting accuracy with overall average error of 2.27%, which improved about average error of 2% as compared to the fuzzy linear regression method.

SIMULATION OF PHOTOVOLTAIC SYSTEMS

Abstract Seasonal and temporal unbalanced demands of electric power are increasing year after year. While an even demand from the load must be maintained in order for the development of a typical solar power generation system for an ordinary usage in the near future, the most important aspect is to develop a low-cost system. In this paper, the load pattern for a factory is chosen as the subject of the research, which has a lot of advantageous factors such as day-load and stability of the day to day load pattern. The two solar power generation systems which are shown below are investigated: one is connected with commercial power source and the other has equipment for charging the battery using low-cost night electric power. Both systems are simulated using meteorological data and are investigated not only from the energy flow in the systems but also from an economic viewpoint based on current constructive cost. In addition, the influence of the arrangement of holiday load patterns on the effective energy generated by photovoltaic panels are also researched.