Miltiadis Alamaniotis

Evolutionary Multiobjective Optimization of Kernel-Based Very-Short-Term Load Forecasting

A useful tool for the efficient management of the electric power grid is the accurate, ahead-of-time prediction-of-load demand. A novel methodology for very-short-term load forecasting is introduced in this paper, and its performance is tested on a set of historical, demand-side, 5-min data. The approach employs an ensemble of kernel-based Gaussian processes (GPs) whose predictions constitute the terms of a linear model. Adoption of a set of cost functions assessing model accuracy allows the formulation of a multiobjective optimization problem with respect to model coefficients. A genetic algorithm (GA) is used to search for a solution based on the previous step data while Pareto optimality theory provides the necessary conditions to identify an optimal one. Thus, it is the optimized linear model that yields the final prediction over the designated time interval. The proposed methodology is examined on 5-min-interval predictions for 30-min-ahead horizon. It is compared with support vector regression (SVR) and autoregressive moving average (ARMA) models as well as the independent GP forecasters on a set of six cost functions. Results clearly promote the proposed forecasting method not only over individual GPs but also over SVR and ARMA.

Genetic Optimal Regression of Relevance Vector Machines for Electricity Pricing Signal Forecasting in Smart Grids

Price-directed demand in smart grids operating within deregulated electricity markets calls for real-time forecasting of the price of electricity for the purpose of scheduling demand at the nodal level (e.g., appliances, machines, and devices) in a way that minimizes energy cost to the consumer. In this paper, a novel hybrid methodology for electricity price forecasting is introduced and applied on a set of real-world historical data taken from the New England area. The proposed approach is implemented in two steps. In the first step, a set of relevance vector machines (RVMs) is adopted, where each RVM is used for individual ahead-of-time price prediction. In the second step, individual predictions are aggregated to formulate a linear regression ensemble, whose coefficients are obtained as the solution of a single objective optimization problem. Thus, an optimal solution to the problem is found by employing the micro-genetic algorithm and the optimized ensemble is employed for computing the final price forecast. The performance of the proposed methodology is compared with performance of autoregressive-moving-average and naïve forecasting methods, as well as to that taken from each individual RVM. Results clearly demonstrate the superiority of the hybrid methodology over the other tested methods with regard to mean absolute error for electricity signal pricing forecasting.

Towards an Energy Internet: A Game-Theoretic Approach to Price-Directed Energy Utilization

The growing interest towards internet-inspired research for power transmission and distribution invariably encounters the barrier of energy storage. Limitations of energy storage can be offset, to a degree, by reliable forecasting of granular demand leading to judicious scheduling involved and incentivized by appropriate pricing signals. The anticipation of energy demand and future system state is of great benefit in scheduling capacities offsetting storage limitations. In this paper, a game is formulated that shows the effect of the synergy between anticipation and price elasticity to achieve lower Peak-to-Average Ratios and minimize waste of energy. The results demonstrate that the final demand signal can be smoother and energy efficiency increased.

Fuzzy-Logic Radioisotope Identifier for Gamma Spectroscopy in Source Search

Detection and identification of radioactive nuclear materials in urban searches can be fully performed with a portable gamma ray detector-spectrometer. Due to limited acquisition time and, as a consequence, low signal to noise ratio (SNR), development of fast and accurate real-time radioisotope identifier (RIID) algorithms is essential for automated source detection. In this paper, we evaluate the performance of fuzzy logic real-time radioisotope identification (FL-RIID) in several urban search scenarios. FL-RIID performance is tested on a database of searches consisting of injections of synthetic sources into experimental nuclear background spectra, acquired in one-second time intervals with a moving sodium iodide (NaI) gamma radiation detector-spectrometer. Performance of FL-RIID is benchmarked against that of maximum-likelihood (ML) fitting method. Demonstrated advantages of FL-RIID over ML in search applications include lower false alarm rate and faster execution time.

A pareto optimization approach of a Gaussian process ensemble for short-term load forecasting

Accurate prediction of load demand remains a challenge for efficient power distribution and becomes critical in the context of smart grid management when the presence of stochastic sources adds to the stochasticity of demand. Short-term load forecasting involving demand prediction in the range of hours or days is of special interest to generators and power customers. A number of methods has been developed for fast and accurate electric power forecasting. Among others, Gaussian process (GP) regression has been used for prediction in the nonlinear problems with promising results. On that direction, an ensemble of Gaussian process regressors modeled as kernel machines is proposed for load forecasting. The use of different kernels accommodates the construction of a group composed of different predictors and its evolution using genetic algorithms. The proposed approach takes the form of a multiobjective problem in which the objectives consist of a set of criteria. In order to optimize all the criteria it needs to use Pareto optimality to identify an accepted solution. The results obtained show that the ensemble of GP predictors outperforms each individual forecaster.

Fuzzy Integration of Support Vector Regression Models for Anticipatory Control of Complex Energy Systems

Anticipatory control systems are a class of systems whose decisions are based on predictions for the future state of the system under monitoring. Anticipation denotes intelligence and is an inherent property of humans that make decisions by projecting in future. Likewise, intelligent systems equipped with predictive functions may be utilized for anticipating future states of complex systems, and therefore facilitate automated control decisions. Anticipatory control of complex energy systems is paramount to their normal and safe operation. In this paper a new intelligent methodology integrating fuzzy inference with support vector regression is introduced. The proposed methodology implements an anticipatory system aiming at controlling energy systems in a robust way. Initially, a set of support vector regressors is adopted for making predictions over critical system parameters. The predicted values are used as input to a two-stage fuzzy inference system that makes decisions regarding the state of the energy system. The inference system integrates the individual predictions at its first stage, and outputs a decision together with a certainty factor computed at its second stage. The certainty factor is an index of the significance of the decision. The proposed anticipatory control system is tested on a real-world set of data obtained from a complex energy system, describing the degradation of a turbine. Results exhibit the robustness of the proposed system in controlling complex energy systems.

Towards smart energy systems: application of kernel machine regression for medium term electricity load forecasting

Integration of energy systems with information technologies has facilitated the realization of smart energy systems that utilize information to optimize system operation. To that end, crucial in optimizing energy system operation is the accurate, ahead-of-time forecasting of load demand. In particular, load forecasting allows planning of system expansion, and decision making for enhancing system safety and reliability. In this paper, the application of two types of kernel machines for medium term load forecasting (MTLF) is presented and their performance is recorded based on a set of historical electricity load demand data. The two kernel machine models and more specifically Gaussian process regression (GPR) and relevance vector regression (RVR) are utilized for making predictions over future load demand. Both models, i.e., GPR and RVR, are equipped with a Gaussian kernel and are tested on daily predictions for a 30-day-ahead horizon taken from the New England Area. Furthermore, their performance is compared to the ARMA(2,2) model with respect to mean average percentage error and squared correlation coefficient. Results demonstrate the superiority of RVR over the other forecasting models in performing MTLF.

A Review of Incentive Based Demand Response Methods in Smart Electricity Grids

Smart electricity grid is a complex system being the outcome of the marriage of power systems with computing technologies and information networks. The information transmitted in the network is utilized for controlling the power flow in the electricity distribution grid. Thus smart grid facilitates a demand response approach, where grid participants monitor and respond to information signals with their electricity demand. This review paper focuses on a subclass of demand response methods and more particularly in incentive based demand response. It aims at providing a review of the existing and proposed methods while briefly explaining their main points and outcomes. In the current approach, the plethora of methods on incentive based demand response is grouped according to the tools adopted to implement the incentives. The overall goal is to provide a comprehensive list of incentive design tools and be a point of inspiration for researchers in the field of incentive based demand response in smart grids.

Intelligent recognition of signature patterns in NRF spectra

Abstract Nuclear resonance fluorescence (NRF) has been considered as a promising method for cargo inspection. Almost all isotopes existing in nature yield a unique NRF spectral signature. NRF signals obtained during cargo inspection are aggregates of various signatures from materials hidden inside. The challenge is to identify individual signatures embedded in this signature aggregation. Background noise and spectra overlap to further complicate the NRF signal analysis. This paper addresses these concerns through an intelligent methodology recognizing signature spectra and, subsequently, identifying cargo materials. The methodology relies on fuzzy logic for pattern identification and evaluation of the weighted options involved in decision making. The intelligent methodology is presented using different simulated NRF signal scenarios. The results obtained demonstrate that the algorithm is highly accurate in most spectra carrying a signal-to-noise ratio (SNR) >20 db. Misses and false alarms were observed for isotopes with only one NRF peak (lead) with SNR <35 db. Extensive parameter testing under different scenarios indicated the existence of parameter couples that maximize the accuracy even for SNR values <20 db. In all cases the algorithm execution time was <0.1 s and was significantly faster than that of the maximum likelihood algorithm.

A Multisignal detection of hazard ousma terials for homeland security

The detection of hazardous materials has been identified as one of the most urgent needs of homeland security, especially in scanning cargo containers at United States ports. To date, special nuclear materials have been detected using neutron or gamma interrogation, and recently the nuclear resonance fluorescence has been suggested. We show a new paradigm in detecting the materials of interest by a method that combines four signals (radiography/computer tomography, acoustic, muon scattering, and nuclear resonance fluorescence) in cargos. The intelligent decision making software system is developed to support the following scenario: initially, radiography or the computer tomography scan is constructed to possibly mark the region(s) of interest. The acoustic interrogation is utilized in synergy to obtain information regarding the ultrasonic velocity of the cargo interior. The superposition of the computer tomography and acoustic images narrows down the region(s) of interest, and the intelligent system guides the detection to the next stage: no threat and finish, or proceed to the next interrogation. If the choice is the latter, knowing that high Z materials yield large scattering angle for muons, the muon scattering spectrum is used to detect the existence of such materials in the cargo. Additionally, the nuclear resonance fluorescence scan yields a spectrum that can be likened to the fingerprint of a material. The proposed algorithm is tested for detection of special nuclear materials in a comprehensive scenario.