Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm

The real-world building can be regarded as a comprehensive energy engineering system; its actual energy consumption depends on complex affecting factors, including various weather data and time signature. Accurate energy consumption forecasting and effective energy system management play an essential part in improving building energy efficiency. The multi-source weather profile and energy consumption data could enable integrating data-driven models and evolutionary algorithms to achieve higher forecasting accuracy and robustness. The proposed building energy consumption forecasting system consists of three layers: data acquisition and storage layer, data pre-processing layer and data analytics layer. The core part of the data analytics layer is a hybrid genetic algorithm (GA) and long-short term memory (LSTM) neural network model for accurate and robust energy prediction. LSTM neural network is adopted to capture the interrelationship between energy consumption data and time. GA is adopted to select the optimal architecture for LSTM neural networks to improve its forecasting accuracy and robustness. The hyper-parameters for determining LSTM architecture include the number of LSTM layers, number of neurons in each LSTM layer, dropping rate of each LSTM layer and network learning rate. Meanwhile, the effects of historical weather profile and time horizon of past information are also investigated. Two real-life educational buildings are adopted to test the performance of the proposed building energy consumption forecasting system. Experiments reveal that the proposed adaptive LSTM neural network performs better than the existing feedforward neural network and LSTM-based prediction models in accuracy and robustness. It also outperforms those LSTM networks whose hyper-parameters are determined by grid search, Bayesian optimisation and PSO. Such accurate energy consumption prediction can play an essential role in various areas, including daily building energy management, decision making of facility managers, building information model designs, net-zero energy operation, climate change mitigation and circular economy.     

基于差分理论的短期负荷预测神经网络模型

电力负荷是受周期性变化以及天气等因素影响的高度非线性系统,而神经网络仅仅对已学习过的模式具有较好的范化能力。为提高神经网络的负荷预测精度,提出先对原始负荷序列进行差分运算以除去其周期性影响,然后依据相似性原理建立RBF神经网络预测模型,仿真实验表明采用该方法短期负荷预测精度有所改善。     

Hybridization of the probabilistic neural networks with feed-forward neural networks for forecasting

Feed-forward neural networks (FFNNs) are among the most important neural networks that can be applied to a wide range of forecasting problems with a high degree of accuracy. Several large-scale forecasting competitions with a large number of commonly used time series forecasting models conclude that combining forecasts from more than one model often leads to improved performance, especially when the models in the ensemble are quite different. In the literature, several hybrid models have been proposed by combining different time series models together. In this paper, in contrast of the traditional hybrid models, a novel hybridization of the feed-forward neural networks (FFNNs) is proposed using the probabilistic neural networks (PNNs) in order to yield more accurate results than traditional feed-forward neural networks. In the proposed model, the estimated values of the FFNN models are modified based on the distinguished trend of their residuals and optimum step length, which are respectively yield from a probabilistic neural network and a mathematical programming model. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way in order to construct a more accurate hybrid model than FFNN models. Therefore, it can be applied as an appropriate alternative model for forecasting tasks, especially when higher forecasting accuracy is needed.     

模糊神经网络在电力短期负荷预测中的应用

提出用于电力短期负荷预测(SILF)的一种模糊神经网络(FNN)方法，该方法针对BP网络收敛速度慢、易导致局部极小值的缺点，将考虑气候、温度、星期类型等影响因素的模糊技术与快速二阶BP网络相结合，并以南方电网负荷预测为例，应用MATLAB蚀语言对系统进行仿真训练，测试结果表明，该方法具有较高的预测精度。     

An artificial neural network (p, d, q) model for timeseries forecasting

Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of time series forecasting problems with a high degree of accuracy. However, despite all advantages cited for artificial neural networks, their performance for some real time series is not satisfactory. Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing forecasters. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in the ensemble are quite different. In this paper, a novel hybrid model of artificial neural networks is proposed using auto-regressive integrated moving average (ARIMA) models in order to yield a more accurate forecasting model than artificial neural networks. The empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by artificial neural networks. Therefore, it can be used as an appropriate alternative model for forecasting task, especially when higher forecasting accuracy is needed.     

A novel method of short-term load forecasting based on multiwavelet transform and multiple neural networks

This paper aims to develop a load forecasting method for short-term load forecasting based on multiwavelet transform and multiple neural networks. Firstly, a variable weight combination load forecasting model for power load is proposed and discussed. Secondly, the training data are extracted from power load data through multiwavelet transform. Lastly, the obtained data are trained through a variable weight combination model. BP network, RBF network and wavelet neural network are adopted as the training network, and the trained data from three neural networks are input to a three-layer feedforward neural network for the load forecasting. Simulation results show that accuracy of the combination load forecasting model proposed in the paper is higher than any one single network model and the combination forecast model of three neural networks without preprocessing method of multiwavelet transform.     

电力大数据下的短期电力负荷预测

电力产业是国民工业系统中重要的产业。在电网运行管理中,对于负荷预测具有非常重要的作用。更加准确的电力负荷预测可以为电网的安全稳定运行、实时进行电网负荷的调度提供了重要依据。特别是在经济方面,精确的电力负荷预测可以优化发、用电电网调度计划,合理调度和分配资源,从而起到使社会效益、经济效益最大化的作用。然而随着中国经济的飞速发展,对电力的需求不断增长,电力负荷本身受诸多因素以及政策影响比如日期、天气、气候、市场等其他因素,这些因素更大大加大了准确进行电力负荷预测的困难性。一直以来,人们一直都致力于提高电力负荷预测的准确性,人工神经网络算法具有泛化、学习能力强等优点,现在该算法已在电力负荷预测领域中得到了广泛应用,并且取得了良好的效果。近年来,人工神经网络领域取得重大突破,涌现出一个新的深度学习研究领域。本文就是基于最新发展的人工神经网络算法,结合实际地区电网数据研究了短期电力负荷预测的相关问题。     

Short‐term electric power load forecasting based on cosine radial basis function neural networks: An experimental evaluation

This article presents the results of a study aimed at the development of a system for short‐term electric power load forecasting. This was attempted by training feedforward neural networks (FFNNs) and cosine radial basis function (RBF) neural networks to predict future power demand based on past power load data and weather conditions. This study indicates that both neural network models exhibit comparable performance when tested on the training data but cosine RBF neural networks generalize better since they outperform considerably FFNNs when tested on the testing data. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 591–605, 2005.     

基于VMD与MOGOA-LSTM的短期负荷预测模型

为了提高短期负荷预测的准确度,降低非平稳信号对模型预测造成的影响,提出一种集数据挖掘和多目标优化网络于一体的短期负荷预测模型。该方法将电力负荷数据通过变分模态分解技术分解成若干个不同频率的模态分量,采用相间重构方法动态确定人工神经网络的训练测试比和神经元设置,采用长短期记忆神经网络对各分量进行模型搭建,并在长短期记忆神经网络的基础上加入多目标蝗虫智能优化算法来优化网络内部参数,累加所有分量模型预测的值,实现短期负荷预测。仿真结果表明,与统计学方和混合模型相比,本文提出的模型在短期预测方面的预测精度较高、泛化能力更强。     

基于模糊逻辑和神经网络的电力负荷预测

设计并实现了神经网络和模糊逻辑相结合的综合预测模型进行短期电力负荷预测。由神经网络和模糊逻辑分别对基本负荷和受天气、节假日影响的负荷进行预测,使其在天气突变等情况下也能达到较高的预测精度。采用此模型对石家庄电力系统负荷进行预测分析,取得了令人满意的结果。     

Highly accurate energy consumption forecasting model based on parallel LSTM neural networks

The main challenges of the energy consumption forecasting problem are the concerns for reliability, stability, efficiency and accuracy of the forecasting methods. The existing forecasting models suffer from the volatility of the energy consumption data. It is desired for AI models that predict irregular sudden changes and capture long-term dependencies in the data. In this study, a novel hybrid AI empowered forecasting model that combines singular spectrum analysis (SSA) and parallel long short term memory (PLSTM) neural networks is proposed. The decomposition with the SSA enhanced the performance of the PLSTM network. According to the experimental results, the proposed model outperforms the state-of-the-art models at different time intervals in terms of both prediction accuracy and computational efficiency.     

Short term load forecasting using daily updated load models

This paper proposes one-day-ahead load forecasting using daily updated weekday load models and weekly updated bias models for everyday-of-the-week loads. The load characteristics are examined first for actual data from Kyushu Electric Power Company and weather stations in Kyushu throughout 1982. Then, according to properties of the loads, the algorithm of the load forecasting is derived. Features of the load forecasting are summarized as follows: (1) according to load curve properties, 24 hourly weekday load models are constructed individually during a 24-h period; (2) the weekday load models, which give estimates for the regression coefficients of weather and other factors, are updated everyday by the exponential weighted least squares method (equivalent to the steady state Kalman filter); (3) by using the bias models, the load forecasts for Saturday, Sunday and Monday patterns are also obtained by the same method used as for the weekday pattern. Based on actual data, the accuracy of the proposed load forecasting was found to be very high, the standard deviation of the relative error of the load forecast being about 3%.     

改进Pi-sigma神经网络及其算法在短期负荷预测中的应用

针对电力负荷的特点,综合考虑历史负荷、天气、日类型等因素的影响,将模糊逻辑和神经网络的长处融合在一起,构建了基于改进Pi-sigma神经网络及其算法的短期负荷预测模型,用于预测预报日的各小时负荷,其中在学习速率的选择、隶属度函数参数的更新等多处进行了改进,进一步减小了预测误差.地区电网的实际应用证明了该算法的有效性.     

Functional networks as a novel data mining paradigm in forecasting software development efforts

This paper proposes a new intelligence paradigm scheme to forecast that emphasizes on numerous software development elements based on functional networks forecasting framework. The most common methods for estimating software development efforts that have been proposed in literature are: line of code (LOC)-based constructive cost model (COCOMO), function point (FP) based on neural networks, regression, and case-based reasoning (CBR). Unfortunately, such forecasting models have numerous of drawbacks, namely, their inability to deal with uncertainties and imprecision present in software projects early in the development life-cycle. The main benefit of this study is to utilize both function points and development environments of recent software development cases prominent, which have high impact on the success of software development projects. Both implementation and learning process are briefly proposed. We investigate the efficiency of the new framework for predicting the software development efforts using both simulation and COCOMO real-life databases. Prediction accuracy of the functional networks framework is evaluated and compared with the commonly used regression and neural networks-based models. The results show that the new intelligence paradigm predicts the required efforts of the initial stage of software development with reliable performance and outperforms both regression and neural networks-based models.     

Pattern similarity-based methods for short-term load forecasting – Part 2: Models

Models for the short-term load forecasting based on the similarity of patterns of seasonal cycles are presented. They include: kernel estimation-based model, nearest neighbor estimation-based models and pattern clustering-based models such as classical clustering methods and new artificial immune systems. The problem of construction of the pattern similarity-based forecasting models and the elements and procedures of the model space are characterized. Details of the model learning and optimization using deterministic and stochastic methods such as evolutionary algorithms and tournament searching are described. Sensitivities of the models to changes in parameter values and their robustness to noisy and missing data are examined. The comparative studies with other popular forecasting methods such as ARIMA, exponential smoothing and neural networks are performed. The advantages of the proposed models are their simplicity and a small number of parameters to be estimated, which implies simple optimization procedures. The models can successfully deal with missing data. The increased number of the model outputs does not complicate their structure. The local nature of the models leads to their simplification and accuracy improvement. The proposed models are strong competitors for other popular univariate methods, which was confirmed in the simulation studies.     

混沌神经网络模型及其应用研究综述

回顾了近年来混沌神经网络模型及其应用的研究进展．首先依据混沌产生的机理,将现有的多种类型混沌神经网络模型归结为4类典型的网络模型,并结合各种网络模型的数学描述来分析各自的机理和特性;然后从复杂问题优化、联想记忆和图像处理、网络与通信、模式识别、电力系统负荷建模和预测5个方面,介绍了混沌神经网络的应用现状;最后评述了混沌神经网络今后的研究方向和研究内容．     

Evolutionary product unit neural networks for short-term wind speed forecasting in wind farms

Combinations of physical and statistical wind speed forecasting models are frequently used in wind speed prediction problems arising in wind farms management. Artificial neural networks can be used in these models as a final step to obtain accurate wind speed predictions. The aim of this work is to determine the potential of evolutionary product unit neural networks (EPUNNs) for improving the accuracy and interpretation of these systems. Traditional neural network and EPUNN approaches have been used to develop different wind speed prediction models. The results obtained using different EPUNN models show that the functional model and the hybrid algorithms proposed provide very accurate prediction compared with standard neural networks used to solve this regression problem. One of the main advantages of the application of these EPUNNs has been the possibility of obtaining some interpretation of the non-linear relation predicted by the model, as will be shown in real data of a wind farm in Spain.     

基于时序分解的用电负荷分析与预测

在智能电网普及的大数据背景下,对电力数据进行精准的分析和预测对电网规划和经济部门的管理决策具有重要的指导意义,但大多数模型都只是在单一的时间尺度上进行研究。针对这一问题提出一种基于时序分解的后向传播算法的循环神经网络预测模型。通过对真实的居民用电消费数据以及外部因素数据统计处理,深入地分析了居民用电特点以及行为规律,并根据其数据的特征以及天气、节假日等外部因素对用户用电行为的影响建立预测模型,对用户未来时段的用电量进行预测。此外,考虑到居民用电消费数据的时序特征在不同时间尺度呈现不同的变化规律,通过时序分解建立预测模型来对用户用电行为的周期性和趋势性进行建模,并通过加权融合达到一起训练的效果,具有一定的协同性,提升预测精度。     

计及光伏及风电并网的电力系统短期负荷预测

为提升光伏、风电等分布式能源大量接入电网后短期电力负荷的预测精度,促进电网消纳能力提升,本文对光伏出力及短期用电负荷采用小波——径向基函数(RBF)神经网络预测方法;对风力发电首先利用总体平均经验模态分解(EEMD)方法对其功率数据分解,再采用BP神经网络、RBF神经网络、小波神经网络、ELMAN神经网络四种神经网络预测方法进行预测,并用粒子群算法(PSO)和灰色关联度(GRA)修正。最后,利用等效负荷的概念,分析光伏、风力发电并网对于短期电力负荷预测的影响,并将三种模型有效结合,得到了考虑光伏及风力发电并网的电力系统短期负荷预测的等效负荷预测模型。实例分析表明,本文所提方法相较于其他方法在该预测项目上具有相对更高的预测精度。     

电力负荷预估的神经网络正则化及其应用

电力负荷预估是目前世界上公认的解决电力资源合理配置的有效措施．而负荷时序预测是实现智能电力系统的关键技术，是一个非常复杂的问题，该问题的解决要求应用大型神经网络．对于庞大的网络，正则化非常重要，需要特别关注，才能实现网络的实用性．为了解决这个问题，我们提出了基于OBD模式的神经网络正则化算法，算法的核心是海森（Hessian）矩阵获取与迭代；讨论了基于该模型的电力负荷预估数值结果．这些结论表明：本文提出的正则化方法的应用有效改善了电力负荷预估的精度．