Prediction interval estimation for electricity price and demand using support vector machines

Uncertainty is known to be a concomitant factor of almost all the real world commodities such as oil prices, stock prices, sales and demand of products. As a consequence, forecasting problems are becoming more and more challenging and ridden with uncertainty. Such uncertainties are generally quantified by statistical tools such as prediction intervals (Pis). Pis quantify the uncertainty related to forecasts by estimating the ranges of the targeted quantities. Pis generated by traditional neural network based approaches are limited by high computational burden and impractical assumptions about the distribution of the data. A novel technique for constructing high quality Pis using support vector machines (SVMs) is being proposed in this paper. The proposed technique directly estimates the upper and lower bounds of the PI in a short time and without any assumptions about the data distribution. The SVM parameters are tuned using particle swarm optimization technique by minimization of a modified Pi-based objective function. Electricity price and demand data of the Ontario electricity market is used to validate the performance of the proposed technique. Several case studies for different months indicate the superior performance of the proposed method in terms of high quality PI generation and shorter computational times.

Changes in corticomotor excitability and inhibition after exercise are influenced by hand dominance and motor demand

Previous studies on handedness have often reported functional asymmetries in corticomotor excitability (CME) associated with voluntary movement. Recently, we have shown that the degree of post-exercise corticomotor depression (PED) and increase in short-interval cortical inhibition (SICI) after a repetitive finger movement task was less when the task was performed at a maximal voluntary rate (MVR) than when it was performed at a submaximal sustainable rate (SR). In the current study, we have compared the time course of PED and SICI in the dominant (DOM) and nondominant (NDOM) hands after an MVR and SR finger movement task to determine the influence of hand dominance and task demand. We tracked motor-evoked potential (MEP) amplitude from the first dorsal interosseous muscle of the DOM and NDOM hand for 20 min after a 10-s index finger flexion-extension task at MVR and SR. For all hand-task combinations, we report a period of PED and increased SICI lasting for up to 8 min. We find that the least demanding task, one that involved index finger movement of the DOM hand at SR, was associated with the greatest change in PED and SICI from baseline (63.6±5.7% and 79±2%, P<0.001, PED and SICI, respectively), whereas the most demanding task (MVR of the NDOM hand) was associated with the least change from baseline (PED: 88.1±3.6%, SICI: 103±2%; P<0.001). Our findings indicate that the changes in CME and inhibition associated with repetitive finger movement are influenced both by handedness and the degree of demand of the motor task and are inversely related to task demand, being smallest for an MVR task of the NDOM hand and greatest for an SR task of the DOM hand. The findings provide additional evidence for differences in neuronal processing between the dominant and nondominant hemispheres in motor control.

Applying choice based conjoint measurement to forcast demand for a new restaurant category

This paper examines the use of Choice Based Conjointexperimentation for forecasting demand for a new restaurant category.The results of the forecasting experiment are compared to demand forexisting restaurant categories to determine whether the choice experimentreplicates actual category shares in the sampled region. The analysisshows that Choice Based Conjoint experiments are able to predictcategory shares for existing restaurant categories. It is then shown howthe approach may be used to estimate demand for a new category.

The myths big media peddle to demand deregulation

This article argues that big media in Australia promote three myths about rural and regional news in Australia as part of their case to deregulate the industry. These myths are that geography no longer matters in local news; that big media are the only ones who can save regional news; and that people in regional Australia can access less news that their city counterparts.

Food demand elasticities for Australia

There is renewed interest in robust estimates of food demand elasticities at a disaggregated level not only to analyse the impact of changing food preferences on the agricultural sector, but also to establish the likely impact of pricing incentives on households. Using data drawn from two national Household Expenditure Surveys covering the periods 1998/1999 and 2003/2004, and adopting an Almost Ideal Demand System approach that addresses the zero observations problem, this paper estimates a food demand system for 15 food categories for Australia. The categories cover the standard food items that Australian households demand routinely. Own-price, cross-price and expenditure elasticity estimates of the Marshallian and Hicksian types have been derived for all categories. The parameter estimates obtained in this study represent the first integrated set of food demand elasticities based on a highly disaggregated food demand system for Australia, and all accord with economic intuition.

Examining performance of aggregation algorithms for neural network-based electricity demand forecasting

The aim of this research is to examine the efficiency of different aggregation algorithms to the forecasts obtained from individual neural network (NN) models in an ensemble. In this study an ensemble of 100 NN models are constructed with a heterogeneous architecture. The outputs from NN models are combined by three different aggregation algorithms. These aggregation algorithms comprise of a simple average, trimmed mean, and a Bayesian model averaging. These methods are utilized with certain modifications and are employed on the forecasts obtained from all individual NN models. The output of the aggregation algorithms is analyzed and compared with the individual NN models used in NN ensemble and with a Naive approach. Thirty-minutes interval electricity demand data from Australian Energy Market Operator (AEMO) and the New York Independent System Operator's web site (NYISO) are used in the empirical analysis. It is observed that the aggregation algorithm perform better than many of the individual NN models. In comparison with the Naive approach, the aggregation algorithms exhibit somewhat better forecasting performance.

Nash Demand Game and the Kalai-Smorodinsky Solution

We introduce two new variations on the Nash demand game. One, like all known Nash-like demand games so far, has the Nash solution outcome as its equilibrium outcome. In the other, the range of solutions depends on an exogenous breakdown probability; surprisingly, the Kalai-Smorodinsky outcome proves to be the most robust equilibrium outcome. While the Kalai- Smorodinsky solution always finishes on top, there is no possible general ranking among the remaining solution concepts considered; in fact, the rest of the solution concepts take their turns at the bottom at various bargaining problems, depending on the specifics of the bargaining setup.

A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings

Electrical load forecasting plays a vital role in order to achieve the concept of next generation power system such as smart grid, efficient energy management and better power system planning. As a result, high forecast accuracy is required for multiple time horizons that are associated with regulation, dispatching, scheduling and unit commitment of power grid. Artificial Intelligence (AI) based techniques are being developed and deployed worldwide in on Varity of applications, because of its superior capability to handle the complex input and output relationship. This paper provides the comprehensive and systematic literature review of Artificial Intelligence based short term load forecasting techniques. The major objective of this study is to review, identify, evaluate and analyze the performance of Artificial Intelligence (AI) based load forecast models and research gaps. The accuracy of ANN based forecast model is found to be dependent on number of parameters such as forecast model architecture, input combination, activation functions and training algorithm of the network and other exogenous variables affecting on forecast model inputs. Published literature presented in this paper show the potential of AI techniques for effective load forecasting in order to achieve the concept of smart grid and buildings.

Forecasting bike sharing demand using fuzzy inference mechanism

Forecasting bike sharing demand is of paramount importance for management of fleet in city level. Rapidly changing demand in this service is due to a number of factors including workday, weekend, holiday and weather condition. These nonlinear dependencies make the prediction a difficult task. This work shows that type-1 and type-2 fuzzy inference-based prediction mechanisms can capture this highly variable trend with good accuracy. Wang-Mendel rule generation method is utilized to generate rule base and then only current information like date related information and weather condition is used to forecast bike share demand at any given point in future. Simulation results reveal that fuzzy inference predictors can potentially outperform traditional feed forward neural network in terms of prediction accuracy.

Uncoupling EPA and DHA in fish nutrition: dietary demand is limited in atlantic salmon and effectively met by DHA alone

Due to the scarcity of marine fish oil resources, the aquaculture industry is developing more efficient strategies for the utilization of dietary omega-3 long-chain polyunsaturated fatty acids (n-3 LC-PUFA). A better understanding of how fish utilize EPA and DHA, typically provided by fish oil, is needed. However, EPA and DHA have different physiological functions, may be metabolized and incorporated into tissues differently, and may vary in terms of their importance in meeting the fatty acid requirements of fish. To address these questions, Atlantic salmon were fed experimental diets containing, as the sole added dietary lipid source, fish oil (positive control), tallow (negative control), or tallow supplemented with EPA, DHA, or both fatty acids to ~50 or 100 % of their respective levels in the positive control diet. Following 14 weeks of feeding, the negative control diet yielded optimum growth performance. Though surprising, these results support the notion that Atlantic salmon requirements for n-3 LC-PUFA are quite low. EPA was largely β-oxidized and inefficiently deposited in tissues, and increasing dietary levels were associated with potential negative effects on growth. Conversely, DHA was completely spared from catabolism and very efficiently deposited into flesh. EPA bioconversion to DHA was largely influenced by substrate availability, with the presence of preformed DHA having little inhibitory effect. These results clearly indicate EPA and DHA are metabolized differently by Atlantic salmon, and suggest that the n-3 LC-PUFA dietary requirements of Atlantic salmon may be lower than reported and different, if originating primarily from EPA or DHA.

A systematic design of interval type-2 fuzzy logic system using extreme learning machine for electricity load demand forecasting

This paper presents a novel design of interval type-2 fuzzy logic systems (IT2FLS) by utilizing the theory of extreme learning machine (ELM) for electricity load demand forecasting. ELM has become a popular learning algorithm for single hidden layer feed-forward neural networks (SLFN). From the functional equivalence between the SLFN and fuzzy inference system, a hybrid of fuzzy-ELM has gained attention of the researchers. This paper extends the concept of fuzzy-ELM to an IT2FLS based on ELM (IT2FELM). In the proposed design the antecedent membership function parameters of the IT2FLS are generated randomly, whereas the consequent part parameters are determined analytically by the Moore-Penrose pseudo inverse. The ELM strategy ensures fast learning of the IT2FLS as well as optimality of the parameters. Effectiveness of the proposed design of IT2FLS is demonstrated with the application of forecasting nonlinear and chaotic data sets. Nonlinear data of electricity load from the Australian National Electricity Market for the Victoria region and from the Ontario Electricity Market are considered here. The proposed model is also applied to forecast Mackey-glass chaotic time series data. Comparative analysis of the proposed model is conducted with some traditional models such as neural networks (NN) and adaptive neuro fuzzy inference system (ANFIS). In order to verify the structure of the proposed design of IT2FLS an alternate design of IT2FLS based on Kalman filter (KF) is also utilized for the comparison purposes.