A new recursive neural network algorithm to forecast electricity price for PJM day‐ahead market

This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day‐ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi‐step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short‐term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R²) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72 h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for short‐term price forecasting. Copyright © 2009 John Wiley & Sons, Ltd.     

Short‐term electric load forecasting using an artificial neural network: case of Northern Vietnam

Short‐term electric load forecasting is an important requirement for electric system operation. This paper employs a feed‐forward neural network with a back‐propagation algorithm for three types of short‐term electric load forecasting: daily peak (valley) load, hourly load and the total load. The forecast has been made for the northern areas of Vietnam using a large set of data on peak load, valley load, hourly load and temperature. The data were used to train and calibrate the artificial neural network, and the calibrated network was used for load forecasting. The results obtained from the model show that the application of neural network to short‐term electric load forecasting problem is very useful with quite accurate results. These results compare well with other similar studies. Copyright © 2004 John Wiley & Sons, Ltd.     

Forecasting method of monthly wind power generation based on climate model and long short-term memory neural network

Predicting wind power generation over the medium and long term is helpful for dispatching departments, as it aids in constructing generation plans and electricity market transactions. This study presents a monthly wind power generation forecasting method based on a climate model and long short-term memory (LSTM) neural network. A nonlinear mapping model is established between the meteorological elements and wind power monthly utilization hours. After considering the meteorological data (as predicted for the future) and new installed capacity planning, the monthly wind power generation forecast results are output. A case study shows the effectiveness of the prediction method.     

应用小波-人工神经网络组合模型研究电力负荷预报

针对负荷时间序列的非线性和多时间尺度特性．提出了将小波分析与人工神经网络相结合进行负荷预报的方法——小波-人工神经网络组合模型。该模型吸取了小波分析的多分辨功能和人工神经网络的非线性逼近能力。以月、日平均负荷预报为例对模型进行验证．结果表明：该模型的拟合、检验精度较高。     

Support vector machines for short‐term electrical load forecasting

Short‐term electrical load forecasting plays a vital role in the electric power industries. It ensures the availability of supply of electricity, as well as providing the means of avoiding over‐ and under‐utilization of generating capacity and therefore optimizes energy prices. Several methods have been applied to short‐term load forecasting, including statistical, regression and neural networks methods. This paper introduces support vector machines, the latest neural network algorithm, to short‐term electrical load forecasting and compares its performance with the auto‐regression model. The results indicate that support vector machines compare favourably against the auto‐regressive model using the same data for building and testing both models based on the root‐mean‐square errors between the actual and the predicted data. Support vector machines allow the training data set to be increased beyond what is possible using the auto‐regressive model or other neural networks methods. Increasing the training data further improves the performance of support vector machines method. Copyright © 2002 John Wiley & Sons, Ltd.     

基于熵值法的组合模型用电量预测方法研究

电力系统的中长期负荷预测是配电网规划的基础,对实现电网的安全经济运行起着重要作用。以年度用电量预测作为研究的对象,年度用电量预测采用4种主要方法,即分别按照年度、季度、月度和行业用电量预测得到对应年用电量预测值,在此基础上再按其发展序列预测结合起来,建立了一种线性组合预测模型。并采用熵值法对组合模型的权系数进行求解,实证分析表明该模型使预测精度得到了明显提高,具有良好的预测效果。     

双碳、双区背景下深圳电力需求预测

  目的  为了适应“双碳”和“双区”新背景下深圳未来用电特征的研究，反映新形势下诸多新因素的影响，需要在传统电力需求预测方法上进行修正，建立新的预测体系。  方法  在电量预测上，一方面以碳强度控制为导向，结合对全社会电气化水平的要求，提出“自上而下”的预测方法；另一方面以改进的细分部门法和新型负荷修正法，进行“自下而上”预测，相互佐证；在负荷预测上，充分考虑需求侧管理、相关削峰手段的影响。  结果  通过量化分析“双碳”目标下能源供应及消费结构调整，“双区”驱动下新基建、产业结构调整和转移等重要因素的影响，对深圳“十四五”及中远期全社会用电量进行预测，并对深圳未来最高负荷和负荷特性发展趋势进行预判。  结论  所提方法为其他地区进行新形势下电力需求预测提供了新思路，预测结果也为深圳后续电源和电网规划及调度运行等提供了重要参考。     

基于长短期记忆神经网络的饱和负荷预测方法及应用

为充分分析关联因素对饱和负荷水平的影响,针对饱和负荷预测不确定性强、时间相关性大的特点,利用长短期记忆神经网络的长期记忆单元与可遗忘机制保存和更新历史用电信息,构建了多输入的长短期记忆神经网络饱和负荷预测模型。首先提取出人口、经济等6个影响因素作为网络模型输入量,采用Adam优化方法训练网络模型,并在多场景下,运用优化后的模型进行饱和负荷预测,结合饱和判据得到最终的饱和时间与用电规模。某省电网的饱和负荷预测结果表明,所建模型及预测方法合理、有效。     

基于最小二乘组合灰色模型的中长期电力负荷预测

电力负荷预测是实现电力系统管理现代化的重要内容,有效的中长期电力负荷预测可为电网运行方式、电源建设规划等提供可靠的决策依据。针对中长期电力负荷预测,综合考虑负荷的历史数据和未来可能走势,在对三种灰色预测模型分析的基础上,以其预测值为解释变量,实际负荷值为被解释变量,通过最小二乘法建立多元回归方程,并通过计量经济检验,以此构造了负荷预测组合模型。将该模型应用于北京市年用电量预测中,结果表明所构造模型具有较高的预测精度,有效可行。     

Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet‐PSO‐NNs

This paper describes the problem of short‐term wind power production forecasting based on meteorological information. Aggregated wind power forecasts are produced for multiple wind farms using a hybrid intelligent algorithm that uses a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on neural network (NN), which is optimized by using particle swarm optimization (PSO) algorithm. To demonstrate the effectiveness of the proposed hybrid intelligent WT + NNPSO model, which takes into account the interactions of wind power, wind speed, wind direction, and temperature in the forecast process, the real data of wind farms located in the southern Alberta, Canada, are used to train and test the proposed model. The test results produced by the proposed hybrid WT + NNPSO model are compared with other SCMs as well as the benchmark persistence method. Simulation results demonstrate that the proposed technique is capable of performing effectively with the variability and intermittency of wind power generation series in order to produce accurate wind power forecasts. Copyright © 2014 John Wiley & Sons, Ltd.     

三峡水库中长期径流预报方法研究

中长期径流预报是充分利用水资源、发挥电站经济效益的有力手段。以三峡水库为研究对象,分别采用周期外延叠加技术、人工神经网络模型、投影寻踪自回归模型和支持向量机回归模型对三峡水库逐月入库径流进行预报。从不同侧面比较分析了这四种方法优劣,并总结各预报模型计算结果的特征及规律,为三峡水库寻求径流预报规律和制定未来中长期调度计划提供了技术支持。     

基于GDP-电能消费耦合关系的中长期电量预测

建立了一种基于用电量和GDP之间耦合关系的中长期电量预测模型。首先利用协整检验和格兰杰（Granger）因果检验,剖析电能消费和经济发展之间的协整关系和因果关系,并建立中长期电量预测模型。然后采用误差修正方法对预测模型进行短期调节,以提高模型的鲁棒性以及预测精度。以某地区1991—2015年的用电量和GDP数据作为算例输入数据,结果表明：通过构建电能消费和经济发展之间的耦合关系,有助于提高预测模型的解释能力,同时含短期调节的中长期用电量预测模型具有更高的预测精度。     

Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)

In this paper, the data analysis and short term load forecasting (STLF) in Iran electricity market has been considered. The proposed method is an improved singular spectral analysis (SSA) method. SSA decomposes a time series into its principal components i.e. its trend and oscillation components, which are then used for time series forecasting, effectively. The employed data are the total load time series of Iran electricity market in its real size and is long enough to make it possible to take properties such as non-stationary and annual periodicity of the market into account. Simulation results show that the proposed method has a good ability in characterizing and prediction of the desired load time series in comparison with some other related methods.     

基于混合神经网络深度学习的短期负荷预测

针对能源互联网环境下用电用户数据量大、多维度这一特点,提出了一种混合神经网络深度学习的短期电力负荷预测方法。首先,考虑常见的电力系统负荷的影响因素,建立多维数据库,并进行偏相关分析,排除其他变量干扰;其次,将LSTM、GRU两种神经网络作为前端神经网络对多维数据库中数据进行处理;最后,采用随机概率剔除与Adam训练优化函数改进的BP神经网络作为末端神经网络,建立负荷预测模型。通过算例仿真对本文方法与传统BP神经网络、LSTM神经网络、GRU神经网络进行了对比,验证了所提方法的有效性。     

Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm

Short-term load forecast (STLF) is a key issue for operation of both regulated power systems and electricity markets. In spite of all performed research in this area, there is still an essential need for more accurate and robust load forecast methods. In this paper, a new hybrid forecast method is proposed for this purpose, composed of wavelet transform (WT), neural network (NN) and evolutionary algorithm (EA). Hourly load time series usually consists of both global smooth trends and sharp local variations, i.e. low- and high-frequency components. WT can efficiently decompose the time series into its components. Each component is predicted by a combination of NN and EA and then by inverse WT the hourly load forecast is obtained. The proposed method is examined on three practical power systems and compared with some of the most recent STLF methods.     

含分布式能源区域电网月最大净负荷概率预测

负荷预测在电网规划和运行中十分重要,大规模分布式能源接入电网参与能量交换,可使电网用户优化其用电模式,但造成区域电网月最大净负荷特性发生根本性改变,增加了月最大净负荷预测的不确定性。为此,结合BP神经网络算法与分位数回归模型,构建了区域电网月最大净负荷的非线性概率预测模型;并利用核密度估计算法计算得到了月最大净负荷概率预测分布曲线;最后,以上海某含分布式能源区域电网为例,验证了该方法的可行性与可靠性。结果表明,该方法可准确刻画月最大净负荷波动特性,为电网规划与负荷管理提供依据。     

Wavelet transform and neural networks for short-term electrical load forecasting

Demand forecasting is key to the efficient management of electrical energy systems. A novel approach is proposed in this paper for short term electrical load forecasting by combining the wavelet transform and neural networks. The electrical load at any particular time is usually assumed to be a linear combination of different components. From the signal analysis point of view, load can also be considered as a linear combination of different frequencies. Every component of load can be represented by one or several frequencies. The process of the proposed approach first decomposes the historical load into an approximate part associated with low frequencies and several detail parts associated with high frequencies through the wavelet transform. Then, a radial basis function neural network, trained by low frequencies and the corresponding temperature records is used to predict the approximate part of the future load. Finally, the short term load is forecasted by summing the predicted approximate part and the weighted detail parts. The approach has been tested by the 1997 data of a practical system. The results show the application of the wavelet transform in short term load forecasting is encouraging.     

A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran

This study presents an integrated algorithm for forecasting monthly electrical energy consumption based on artificial neural network (ANN), computer simulation and design of experiments using stochastic procedures. First, an ANN approach is illustrated based on supervised multi-layer perceptron (MLP) network for the electrical consumption forecasting. The chosen model, therefore, can be compared to that of estimated by time series model. Computer simulation is developed to generate random variables for monthly electricity consumption. This is achieved to foresee the effects of probabilistic distribution on monthly electricity consumption. The simulated-based ANN model is then developed. Therefore, there are four treatments to be considered in analysis of variance (ANOVA), which are actual data, time series, ANN and simulated-based ANN. Furthermore, ANOVA is used to test the null hypothesis of the above four alternatives being statistically equal. If the null hypothesis is accepted, then the lowest mean absolute percentage error (MAPE) value is used to select the best model, otherwise the Duncan method (DMRT) of paired comparison is used to select the optimum model which could be time series, ANN or simulated-based ANN. In case of ties the lowest MAPE value is considered as the benchmark. The integrated algorithm has several unique features. First, it is flexible and identifies the best model based on the results of ANOVA and MAPE, whereas previous studies consider the best fitted ANN model based on MAPE or relative error results. Second, the proposed algorithm may identify conventional time series as the best model for future electricity consumption forecasting because of its dynamic structure, whereas previous studies assume that ANN always provide the best solutions and estimation. To show the applicability and superiority of the proposed algorithm, the monthly electricity consumption in Iran from March 1994 to February 2005 (131 months) is used and applied to the proposed algorithm.     

基于分数阶灰色Elman组合模型的中长期负荷预测

针对电力系统中长期负荷预测样本少、间隔时间长、影响因素多等问题,提出基于分数阶灰色Elman的组合预测模型,首先针对负荷预测样本少、增长趋势明显的特点,利用分数阶灰色模型弱化原始序列的随机性,降低解的扰动界,其次利用Elman神经网络模型适应性与学习能力强的特点来解决负荷的非线性及影响因素复杂的问题,然后根据最优模型赋予二者最优权值,得到最终的组合模型,最后采用傅里叶级数残差校正模型修正组合模型的误差。仿真结果表明,本文提出的方法可有效拟合负荷的变化趋势,提升负荷预测的准确度。     

Electric load analysis using an artificial neural network

Load forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artificial neural net to an hourly based load forecasting application for a small electric grid on an Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of a neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are affected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi‐layer perceptron type built on three layers trained with a back‐propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric load forecasting applications can be considered an attractive option. Copyright © 2005 John Wiley & Sons, Ltd.