Bijay Neupane

Artificial Neural Network-based electricity price forecasting for smart grid deployment

A deregulated electricity market is one of the keystones of up-and-coming smart grid deployments. In such a market, forecasting electricity prices is essential to helping stakeholders with the decision making process. Electricity price forecasting is an inherently difficult problem due to its special characteristics of dynamicity and nonstationarity. In our research, we use an Artificial Neural Network (ANN) model on carefully crafted input features for forecasting hourly electricity prices for the next 24 hours. The input features are selected from a pool of features derived from information such as past electricity price data, weather data, and calendar data. A wrapper method for feature selection is used in which the ANN model is continuously trained and updated in order to select the best feature set. The performance of the proposed method is evaluated and compared with the published results of the state-of-the-art Pattern Sequence-based Forecasting (PSF) method on the same data sets and our method is observed to provide superior results.

Towards flexibility detection in device-level energy consumption

The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for load- and flexibility-detection and -prediction at the device level.

A New Image Edge Detection Method using Quality-based Clustering

Due to the various limitations of existing edge detection methods, finding better algorithms for edge detection is still an active area of research. Many edge detection approaches have been proposed in the literature but in most cases, the basic approach is to search for abrupt change in color, intensity or other properties. Unfortunately, in many cases, images are corrupted with different types of noise which might cause sharp changes in some of these properties. In this paper, we propose a new method for edge detection which uses k-means clustering, and where different properties of image pixels were used as features. We analyze the quality of the different clusterings obtained using different k values (i.e., the predefined number of clusters) in order to choose the best number of clusters. The advantage of this approach is that it shows higher noise resistance compared to existing approaches. The performance of our method is compared against those of other methods by using images corrupted with various levels of “salt and pepper” and Gaussian noise. It is observed that the proposed method displayed superior noise resilience.

Evaluating the Value of Flexibility in Energy Regulation Markets

In this paper, we perform an econometric analysis on the benefits of introducing flexibility in the Danish/Nordic regulating power market. The paper investigates the relationships between market power prices and regulation volumes, in order to quantify the effects of flexibility on regulating power prices. Further, we analyze the benefit for various types of flexibility and market objectives, to detect the type of energy flexibility that maximizes the benefits. Results show that if 3.87% of total demand is flexible, the market can reduce the regulation cost by 49% and the regulation volume by 29.4%.

A Novel Ensemble Learning-Based Approach for Click Fraud Detection in Mobile Advertising

By diverting funds away from legitimate partners (a.k.a publishers), click fraud represents a serious drain on advertising budgets and can seriously harm the viability of the internet advertising market. As such, fraud detection algorithms which can identify fraudulent behavior based on user click patterns are extremely valuable. Based on the BuzzCity dataset, we propose a novel approach for click fraud detection which is based on a set of new features derived from existing attributes. The proposed model is evaluated in terms of the resulting precision, recall and the area under the ROC curve. A final ensemble model based on 6 different learning algorithms proved to be stable with respect to all 3 performance indicators. Our final model shows improved results on training, validation and test datasets, thus demonstrating its generalizability to different datasets.

Generation and Evaluation of Flex-Offers from Flexible Electrical Devices

There exists an immense potential in utilizing the demand reduction and shifting potential (flexibility) of household devices to confront the challenges of intermittent Renewable Energy Sources. However, a widely accepted general flexibility extraction and evaluation process is missing. This paper proposes a generalized Flex-offer Generation and Evaluation Process (FOGEP) that extract flexibility from wet-devices (e.g. dishwashers), electric vehicles, and heat pumps and capture it in a unified model, a so-called flex-offer. The proposed process analyses the past consumption behavior of a device to automatically capture flexibility in its usage. It utilizes two device-level forecasting techniques and algorithms to capture various attributes and temporal patterns required for flexibility extraction. Further, the paper evaluates the performance of FOGEP regarding the accuracy of the extracted flexibility and performs an economic assessment to identify the device-specific best market to trade flexibility. The experimental results, based on real-world measurement data, show that household devices have up to 32% of reduction and 15 hours of shifting flexibility in their energy demands. Further, FOGEP can extract flexibility with up to 98% accuracy. The flexibilities can provide up to 51% and 11% savings in the spot and regulating market for Balance Responsible Party (BRP) and/or consumer, respectively.

Data Analytics for Renewable Energy Integration

The increasing drive towards green energy has boosted the installation of Renewable Energy Sources (RES). Increasing the share of RES in the power grid requires demand management by flexibility in the consumption. In this paper, we perform a state-of-the-art analysis on the flexibility and operation patterns of the devices in a set of real households. We propose a number of specific pre-processing steps such as operation stage segmentation, and aberrant operation duration removal to clean device level data. Further, we demonstrate various device operation properties such as hourly and daily regularities and patterns and the correlation between operating different devices. Subsequently, we show the existence of detectable time and energy flexibility in device operations. Finally, we provide various results providing a foundation for loadand flexibility-detection and -prediction at the device level.

Utilizing Device-level Demand Forecasting for Flexibility Markets

The uncertainty in the power supply due to fluctuating Renewable Energy Sources (RES) has severe (financial and other) implications for energy market players. In this paper, we present a device-level Demand Response (DR) scheme that captures the atomic (all available) flexibilities in energy demand and provides the largest possible solution space to generate demand/supply schedules that minimize market imbalances. We evaluate the effectiveness and feasibility of widely used forecasting models for device-level flexibility analysis. In a typical device-level flexibility forecast, a market player is more concerned with the utility that the demand flexibility brings to the market, rather than the intrinsic forecast accuracy. In this regard, we provide comprehensive predictive modeling and scheduling of demand flexibility from household appliances to demonstrate the (financial and otherwise) viability of introducing flexibility-based DR in the Danish/Nordic market. Further, we investigate the correlation between the potential utility and the accuracy of the demand forecast model. Furthermore, we perform a number of experiments to determine the data granularity that provides the best financial reward to market players for adopting the proposed DR scheme. A cost-benefit analysis of forecast results shows that even with somewhat low forecast accuracy, market players can achieve regulation cost savings of 54% of the theoretically optimal.