(Pdf) groundwater level forecasting using svm-pso electricity bill calculator


Groundwater level is regarded as an environmental indicator to quantify ground-water resources and their exploitation. In general, Groundwater systems are characterized by complex and nonlinear features. Gaussian Process Regression (GPR) approach is employed in the present study to investigate its applicability in probabilistic gasbuddy trip forecasting of monthly groundwater level fluctuations at two shallow unconfined aquifers located in the Kumaradhara river basin near Sullia Taluk, India. A series of monthly groundwater level observations monitored during the period 2000 to 2013 is utilized for the simulation. Univariate time-series GPR and Adaptive Neuro Fuzzy Inference System (ANFIS) models are simulated and applied for multi-step lead time forecasting of groundwater levels. Individual per-formance of the GPR and ANFIS models are comparatively evaluated using various statistical indices. In overall, simulation results reveal that GPR model provided reasonably accurate predictions than that of ANFIS during both training and testing phases. Thus, an effective GPR model is found to generate more precise probabilistic forecasts of groundwater levels.

Because power system load forecasting was a uncertain, nonlinear, dynamic and complicated system, it was difficult to describe such a nonlinear characteristics of this system by traditional methods, so the load forecasting could not be accurately forecasted. The authors presented a novel load forecasting method in which an improved Support Vector Machines (SVM) algorithm based on time sequence was applied and the principle of Structural Risk Minimization (SRM) was embedded into the SVM, therefore, on the basis of learning by fewer samples the presented method could conduct fast and accurate load forecasting with other samples fitting load forecasting. The presented method was more generalized and its dependence on experience was weakened. In the time sequence the trend component and periodical component were considered to make the load forecasting model more coincident with the features of power loads. Applying the presented method to actual load forecasting, the comparison among the forecasted results and the true shows that the presented method is feasible and effective.

A hybrid model integrating artificial neural networks and support vector wholesale electricity prices by state regression was developed for daily rainfall prediction. In the modeling process, singular spectrum analysis was first adopted to decompose the raw rainfall data. Fuzzy C-means clustering was then used to split the training set into three crisp subsets which may be associated with low-, medium- and high-intensity rainfall. Two local artificial gas jeans usa neural network models were involved in training and predicting low- and medium-intensity subsets whereas a local support vector regression model was applied to the high-intensity subset. A conventional artificial neural network model was selected as the benchmark. The artificial neural network with the singular spectrum analysis was developed for the purpose of examining the singular spectrum analysis technique. The models were applied to two daily rainfall series from China at 1-day-, 2-day- and 3-day-ahead forecasting horizons. Results showed that the hybrid support vector regression model performed the best. The singular spectrum analysis model also exhibited considerable accuracy in rainfall forecasting. Also, two methods to filter reconstructed components of singular spectrum analysis, supervised and unsupervised approaches, were compared. The unsupervised method appeared more effective where nonlinear dependence between model inputs and output can be considered.

Associative 6 gases memory networks (AMNs) based on radial basis functions (RBFs) are often used to model complex and nonlinear systems, as they can approximate nonlinear systems with arbitrary accuracy and can be trained from data. However, good generalization results can only be obtained if the structure of the RBF network is suitably chosen. An approach to select the structure of the RBF networks based on the support vectors (SVs) of the support vector machine (SVM) has been proposed. The main advantage of this approach is that the structure of the network can be obtained objectively, as the SVs of the SVM are obtained from a constrained optimization for a given error bound. For convenience, this class of AMNs is referred to as support vector radial basis function networks (SVRBFNs). In this paper, the modelling of the relationship between rainfall and river discharges of the Fuji river using the SVRBFN is presented. As there are large outliers in the modelling errors arising from the data collection process, they are removed first before retraining the SVRBFN using the adjusted data, in order to obtain a better approximation of the relationship between rainfall and river discharges. The generalization ability of the SVRBFN is verified using the test data that are the most recent not used in the training of the network. The prediction of river discharges for given rainfalls can be computed from the SVRBFN, which can provide early warning of severe river discharges when there is heavy and prolonged rainfall.

To address the core needs and competitive demand within the field of prognostics and health management (PHM), an innovative research direction is described and demonstrated. The new methodology is a hybrid combination of physics-based and data-driven PHM. The process of combining intelligent sensor based monitoring and diagnostics to enhance performance and reliability throughout a system’s life … [Show full abstract] is the basis of data-drive PHM. Use of physics-based models to predict system response is the basis of physics-based PHM. Both approaches have distinct advantages and disadvantages gas examples when applied to specialized integrated electrical, mechanical, and software components. Individually, they fall short of delivering broad capability and value across the entire range of existing and future intelligent devices. While there are many methods and algorithms for health management, such as Fuzzy Logic Predictions, Bayesian Belief Networks, Artificial Neural Networks, and Autoregressive Moving Average to name a few, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are one of the most effective approaches that can be used for both data-driven and physics-based PHM diagnostics and prognostics. These ANFISs can be trained by applying sensor data as boundary conditions in physics-based computer aided analysis over the entire operating envelopes of a system. Kalman filter and particle filter are systematic sequential fusion frameworks to combine the diagnostic results from the measurement signals and the degradation 5 gas laws models. Hybrid PHM systems that utilize both physics-based and data-driven approaches are a relatively unexplored multidisciplinary methodology. By using this hybrid approach an established ANFIS model can interpolate between input parameters and desired output information to optimize system function as well as facilitate ongoing self-training using new input data. Successful integration of physics-based and data-driven models and its application will be a truly transformative breakthrough in intelligent system development. This paper shows the results of preliminary methodology development and application on gas turbine engines and gearboxes. Read more

The prediction of groundwater levels in a basin is of immense importance for the management of groundwater resources. In this study, support vector machines (SVMs) is used to construct a ground water level forecasting system. Further the proposed SVM-PSO model is employed in estimating the groundwater level of Rentachintala region of Andhra Pradesh in India. The SVM-PSO model with various input … [Show full abstract] structures is constructed and the best structure is determined using the k-fold cross validation method. Further particle swarm optimisation function is adapted in this study to determine the optimal values of SVM parameters. Later, the performance of the SVM-PSO model is compared with the autoregressive moving average model (ARMA), artificial neural networks (ANN) and adaptive neuro fuzzy inference system (ANFIS). The results indicate that SVM-PSO is a far better technique for predicting groundwater levels as it provides a high degree of accuracy and reliability. Read more

Accurate short-term wind power forecast is very important for electricity sources usa reliable and efficient operation of power systems with high wind power penetration. There are many conventional and artificial intelligence methods that have been developed to achieve accurate wind power forecasting. Time-series based algorithms are known to be simple, robust, and have been used in the past for forecasting with some … [Show full abstract] level of success. Recently some researchers have advocated for artificial-intelligence based methods such as Artificial Neural Networks (ANNs), Fuzzy Logic, etc., for forecasting because of their flexibility. This paper presents a comparison of conventional and two artificial intelligence methods for wind power forecasting. The conventional method discussed in this paper is the Autoregressive Moving Average (ARMA) which is one of the most robust and simple time-series methods. The artificial intelligence methods are Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference Systems (ANFIS). Simulation results for very-short-term and short-term forecasting show that ANNs and ANFIS are suitable for the electricity news australia very-short-term (10 minutes ahead) wind speed and power forecasting, and the ARMA is suitable for the short-term (1 hour ahead) wind speed and power forecasting. Read more