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Short-term electricity price forecasting in deregulated electricity markets has been studied extensively in recent years but without significant reduction in price forecasting errors. Also demand-side management and short-term scheduling operations in smart grids do not require strictly very accurate forecast and can be executed with certain practical price thresholds. This paper, therefore, presents a multikernel extreme learning machine (MKELM) for both short-term electricity price forecasting and classification according to some prespecified price thresholds. The kernel ELM does not require the hidden layer mapping function to be known and produces robust prediction and classification in comparison with the conventional ELM using random weights between the input and hidden layers. Further in the MKELM formulation, the linear combination of the weighted kernels is optimized using vaporization precipitation-based water cycle algorithm (WCA) to produce significantly accurate electricity price prediction and classification. The combination of MKELM and WCA is named as WCA-MKELM in this work. To validate the effectiveness of the proposed approach, three electricity markets, namely PJM, Ontario and New South Wales, are considered for electricity price forecasting and classification producing fairly accurate results. [ABSTRACT FROM AUTHOR]

Probabilistic forecasting is an appropriate tool that helps electricity markets participants to improve their decision making. Due to changes in electricity prices, the point forecasting accuracy cannot be guaranteed. Hence, participants are more interested in results of probabilistic forecasting methods such as prediction intervals method. In this paper, a hybrid approach for probabilistic electricity price forecasting is presented. This model is based on using improved clonal selection algorithm and extreme learning machine for neural networks training process and wavelet preprocess. The wavelet is utilized to decompose data into well behaved subsets, which increases accuracy of the model. Also, due to the high required computational time for training the neural networks, autocorrelation function is used to reduce the number of neural networks inputs. Finally, in order to evaluate the proposed probabilistic forecasting method, the Ontario and Australian electricity markets data are used. [ABSTRACT FROM AUTHOR]

Electricity price is an important aspect in a restructured power market. Because of nonlinearity, nonstationary, and volatility of electricity price, it is highly essential to forecast the price on a short-term scale. This paper presents an efficient method based on a hybrid functional link dynamic neural network (DNN) trained by an adaptive robust unscented Kalman filter (UKF). The proposed method forecasts hourly prices for the day-ahead electricity market. The functional block helps to introduce nonlinearity by expanding the input space to higher-dimensional space through a basis function without using any hidden layer like multilayer perceptron structure. The DNN includes one or more infinite impulse response filters in the forward path providing feedback connections between outputs and inputs. This allows signal flow in both forward and backward directions, giving the network a dynamic memory useful to mimic dynamic systems. Also to improve the accuracy of the forecast, the noise covariance matrices of the UKF are adapted recursively. The proposed method is tested on PJM electricity market, and the residuals mean absolute error is compared with other forecasting methods, indicating the improved accuracy of the approach and its suitability to produce a real-time forecast. Further, to compare the accuracy of the forecast, an alternative UKF noise covariance optimization is attempted using differential evolution. [ABSTRACT FROM AUTHOR]

Price forecasting of oil products and natural gas is of great interest due to their essential roles in modern industry and human lives. Existing studies on energy price forecasting are mostly concerned with individual energy markets, with less consideration of their interaction. This paper presents a bi-graph attention network (BiGAT) approach for energy price forecasting of oil products and natural gas, aiming at exploiting the price correlations of these energy sources for prediction. Specifically, we introduce the concordance graph and the causality graph into the BiGAT model to quantify the Kendall's rank correlation and the convergence cross mapping causality of the energy prices. To facilitate training the BiGAT model with the energy price time series of multiple time scale nature, we employ the boosted Hodrick–Prescott (bHP) filter to decompose the price data into slow- and fast-varying parts for learning, respectively. As the original bHP filtering algorithm involves computing an inverse matrix of the data size, it is not in favor of using in expanding or streaming data scenarios directly. Here, we also devise an incremental bHP filtering algorithm that applies to data of arbitrary finite size in a recursive manner, requiring the computation of a third-order initial inverse matrix only. Experimental results on empirical data show that the forecasting accuracy of our model is significantly better than other considered models, and the proposed incremental algorithm effectively extends the application scenarios of the bHP filter. [ABSTRACT FROM AUTHOR]

Tourist arrivals forecasting has become an increasingly hot issue due to its important role in the tourism industry and hence the whole economy of a country. However, owing to the complex characteristics of tourist arrivals series, such as seasonality, randomness, and non-linearity, forecasting tourist arrivals remains a challenging task. In this paper, a hybrid model of dual decomposition and an improved fuzzy time series method is proposed for tourist arrivals forecasting. In the novel model, two stages are mainly involved, i.e., dual decomposition and integrated forecasting. In the first stage, a dual decomposition strategy, which can overcome the potential defects of individual decomposition approaches, is designed to fully extract the main features of the tourist arrivals series and reduce the data complexity. In the second stage, a fuzzy time series method with fuzzy C-means algorithm as the discretization method is developed for prediction. In the empirical study, the proposed model is implemented to predict the monthly tourist arrivals to Hong Kong from USA, UK, and Germany. The results show that our hybrid model can obtain more accurate and more robust prediction results than benchmark models. Relative to the benchmark fuzzy time series models, the hybrid models using traditional decomposition methods and strategies, as well as the traditional single prediction models, our proposed model shows a significant improvement, with the improvement percentages at about 80, 70, and 50%, respectively. Therefore, we can conclude that the proposed model is a very promising tool for forecasting future tourist arrivals or other related fields with complex time series. [ABSTRACT FROM AUTHOR]

Knowing the demand for electrical consumption beforehand is important for efficient energy programming policies that can help with climate change, life cycle-costs, and optimal primary resource extraction. In this paper, we propose a framework to improve forecasting performance of high frequency electrical consumption data. We use different models for each day of the week, and then compose them to obtain the total forecast. We apply both machine learning (Long-Short Term Memory network) and econometric models (AutoRegressive Integrated Moving Average and Holtz-Winters) that consider time dependence in the data comparing model performance. We find that a classical ARIMA model outperforms other models; however, in applying the proposed framework, LSTM manages to outperform all other models. The results are statistically significant as indicated by the Model Confidence Set test constructed for Mean Absolute Percentage Error and Mean Square Error. [ABSTRACT FROM AUTHOR]

In the process of building electrical load data collection, it is inevitable to introduce different kinds of noises, which makes the observation values deviate from the actual values, thus resulting in high levels of uncertainties. And such uncertainties make it difficult to achieve accurate point prediction of the short-term building electrical load. To improve the rationality of the prediction results and offer more effective information for decision makers, this paper proposes a novel multi-objective algorithm optimized modular fuzzy method which can accomplish the interval prediction for the short-term electrical load. First, one novel single-input-rule-modules (SIRMs)-based distributed interval fuzzy model (SIRM-DIFM) is proposed by replacing the original functional weights of the traditional SIRMs-based fuzzy inference system (SIRM-FIS) with the interval functional weights. Then, a data-driven learning scheme is presented for constructing the SIRM-DIFM. This learning sheme includes two main steps. The first step utilizes the iterative least square method to generate fuzzy rules for the SIRMs and determine the centers of the interval functional weights, while in the second step, the genetic algorithm (GA)-based multi-objective optimization algorithm is adopted to determine the widths of the interval functional weights. Through these two steps, accurate point estimation and reasonable interval prediction results can be achieved. Finally, two building electrical load prediction experiments are conducted to verify the effectiveness of the presented SIRM-DIFM. Simulation results indicate that the proposed SIRM-DIFM can compensate the shortcomings of the low accuracy of the point estimation and the predicted interval can effectively cover the observed data, providing the decision-makers more reliable and useful information. [ABSTRACT FROM AUTHOR]

Time series modelling is gaining spectacular popularity in the prediction process of decision making, with applications including real-world management and engineering. However, for short time series, prediction has to face unavoidable limitation for modelling extremely complex systems. It has to apply inadequate and incomplete data from short time to predict unknown observations. With such limited data source, existing techniques, such as statistical modelling or machine learning methods, fail to predict short time series effectively. To address this problem, this paper provides a global framework for short time series modelling predictions, integrating the rolling mechanism, grey model, and meta-heuristic optimization algorithms. In addition, dragonfly algorithm and whale optimization algorithm are investigated and deployed to optimize the framework by enhancing its predicting accuracy with less computational costs. To verify the performance of the proposed framework, three industrial cases are introduced as simulation experiments in this paper. Experimental results confirm that the framework solves corresponding short time series modelling predictions with greater accuracy and speed than the standard GM(1,1) models. The source codes of this framework are available at: https://github.com/zhesencui/HybridRollingGreyFramework.git. [ABSTRACT FROM AUTHOR]

Almost every organization around the globe is working with uncertainty due to inevitable changes and growth in every sphere of life. These changes affect directly or indirectly the stock market prices which makes forecasting a challenging task. So, the need for reliable, cost-effective, and accurate forecasting models significantly arises to reduce risk and uncertainty in stock market investment. Different time series models have been proposed by data scientists and researchers for accurate prediction of the future with the least errors. Econometric autoregressive time series models such as autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models have established forecasting models capable of generating accurate forecasts. Wavelet methods, being capable of handling nonlinear data, combined with autoregressive models generate more accurate forecasts. In this present study, soft computing models of discreet wavelet transformation and wavelet denoising combined with autoregressive models are developed to forecast the weekly and daily closing prices of the BSE100 S&P Sensex index. Statistical error analysis of the forecasting outcomes of coupled models has been made to evaluate the performance of the prediction of these models. The prediction results reveal that soft computing methods coupled with autoregressive models (wavelet-ARIMA and wavelet denoise-ARIMA) generate considerably accurate forecasts as compared to baseline models (simple regression, ARMA and ARIMA models) and coupled models (wavelet-ARMA and wavelet denoise-ARMA models). [ABSTRACT FROM AUTHOR]

Electricity, a kind of clean energy, has been widely used in people's production and daily life. However, it is very difficult to estimate the electricity energy production in advance and store the rest of the electric energy due to the climate, environment, population and other factors. Based on data preprocessing and artificial intelligence optimization algorithm, this paper introduces a combined forecasting method. The proposed method contains six individual methods, and each individual method has its own usage. Singular spectrum analysis (SSA) is adopted to reduce noise from the original data; three individual forecasting methods, Jordan neural network, the echo state network, least squares support vector machine, are applied to obtain the intermediate forecasting results; two optimization algorithms, particle swarm optimization and simulated annealing, are used to optimize the parameters of the combined model. This paper not only validates the superiority of the combined model compared to the single predictive model through the simulation experiments of power load data and electricity price data. The mean absolute percent error (MAPE) of the combined power load and electricity price forecast results are 1.14% and 7.58%, respectively, which are higher than the MAPE error of the corresponding single models prediction results. It has also been verified that the process of eliminating noise by the SSA plays a positive role in the accuracy of the combined forecasting model. In addition, two series of experiments on the power load data lead to two very interesting conclusions. One of the conclusions is that as the size of the test data increases, the prediction accuracy of the model decreases; the other is that the predicted result calculated through the optimized combined weight is better than the combined result calculated using the average weight, and the average weight is used. Weighted combination does not improve the prediction accuracy of a single model. [ABSTRACT FROM AUTHOR]

Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load switching and power grid. Thus, there have been models developed to estimate the electricity demand. However, inaccurate demand forecasting may raise the operating cost of electric power sector, which means that it would waste considerable money. In this paper, a novel modeling framework was proposed for forecasting electricity demand. Sample entropy was developed to identify the nonlinearity and uncertainty in the original time series, after that redundant noise was removed through a decomposition technique. Besides, the most optimal modes of original series and the optimal input form of the model were determined by the feature selection method. Finally, electricity demand series can be conducted forecasting through least squares support vector machine tuned by multi-objective sine cosine optimization algorithm. The case studies of Australia demonstrated that the proposed framework can ensure high accuracy and strong stability. Thus, it can be considered as a useful tool for electricity demand forecasting. [ABSTRACT FROM AUTHOR]

A new hybrid model combining the kernel functions along with the random vector functional link neural network (RVFLN) is proposed in this paper for an effective solar power prediction. The conventional RVFLN is known for its fast learning speed, simple architecture and good generalization capabilities and allows direct connection between input and output nodes along with nonlinear enhancement nodes with random weights. However, the bottleneck of selecting the number of hidden enhancement nodes and mapping functions is still a challenging problem. To overcome these deficiencies of the conventional RVFLN, kernel functions are used for both the direct links and the hidden nodes to provide better stability, generalization and regression accuracy. Instead of using a single kernel for the enhancement nodes, this paper proposes an optimal kernel function that comprises a linear combination of weighted local kernel and a global kernel to improve the prediction accuracy of the solar power generation. This optimal kernel will be known as multi-kernel RVFLN (MK-RVFLN), and its parameters are optimized using an efficient metaheuristic evaporation-based water cycle (EVWCA-MKRVFLN) to provide accurate prediction of solar power. To validate its superior prediction accuracy, two solar power plants of 25 and 100 MW capacity in the states of New York and California are considered for 5-min- and 60-min-ahead prediction in the months of January, April, July and October. The result analysis shows that the MK-RVFLN algorithm attains better performance than many other techniques. [ABSTRACT FROM AUTHOR]

Many forecasting models are built on neural networks. The key issues in these models, which strongly translate into the accuracy of forecasts, are data representation and the decomposition of the forecasting problem. In this work, we consider both of these problems using short-term electricity load demand forecasting as an example. A load time series expresses both the trend and multiple seasonal cycles. To deal with multi-seasonality, we consider four methods of the problem decomposition. Depending on the decomposition degree, the problem is split into local subproblems which are modeled using neural networks. We move from the global model, which is competent for all forecasting tasks, through the local models competent for the subproblems, to the models built individually for each forecasting task. Additionally, we consider different ways of the input data encoding and analyze the impact of the data representation on the results. The forecasting models are examined on the real power system data from four European countries. Results indicate that the local approaches can significantly improve the accuracy of load forecasting, compared to the global approach. A greater degree of decomposition leads to the greater reduction in forecast errors. [ABSTRACT FROM AUTHOR]

In power industry and management, given the peculiarity and high complexity of the time series, it is highly requested to make models more flexible and well adapted to the data, in order to give them the ability to capture and recognize the most complex patterns, and consequently gain more accuracy in forecasting. To fulfill this aim, we define the seasonal autoregressive neural network (SAR-NN) as a dynamic feedforward artificial neural network (ANN), essentially conceived to forecast electricity prices. The SAR-NN is an ANN-based autoregressive model that considers that autoregressors are only those that are lagged by a multiple of the period p of the dominating seasonality. Moving forward this neural network, step-by-step, allows to generate multiple-steps-ahead reliable forecasts. This model is specifically designed to robustly overcome the strong seasonal effects and the other nonlinear patterns that often harm ANNs forecasting performance. The strategy is tested and compared to a number of homologous models throughout empirical experiments. As a case study, we focus on the Nord Pool Scandinavian power market, which is one of the most mature energy markets worldwide. [ABSTRACT FROM AUTHOR]

Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural networks which involve the use of radial basis activation function produce more successful forecasting results, Gaussian activation function has not been used for multiplicative neuron model yet. In this study, rather than using a sigmoid activation function, Gaussian activation function was used in multiplicative neuron model artificial neural network. The weights of artificial neural network and parameters of activation functions were optimized by guaranteed convergence particle swarm optimization. Two major contributions of this study are as follows: the use of Gaussian activation function in multiplicative neuron model for the first time and the optimizing of central and propagation parameters of activation function with the weights of artificial neural network in a single optimization process. The superior forecasting performance of the proposed Gaussian activation function-based multiplicative neuron model artificial neural network was proved by applying it to real-life time series. [ABSTRACT FROM AUTHOR]