Lessons learnt from mining meter data of residential consumers

Tracking end-users' usage patterns can enable more accurate demand forecasting and the automation of demand response execution. Accordingly, more advanced applications, such as electricity market design, integration of distributed generation and theft detection can be developed. By employing data mining techniques on smart meter recordings, the suppliers can efficiently investigate the load patterns of consumers. This paper presents applications where data mining of energy usage can derive useful information. Higher demands, on one side, and the energy price increase on the other side, have caused serious issues with regards to electricity theft, especially among developing countries. This phenomenon leads to considerable operational losses within the electrical network. In order to identify illegal residential consumers, a new method of analysing and identifying electricity consumption patterns of consumers is proposed in this paper. Moreover, the importance of data mining for analysing the consumer's usage curves was investigated. This helps to determine the behaviour of end-users for demand response purposes and improve the reliability and security of the electricity network. Clustering load profiles for large scale energy datasets are discussed in detail

Image authentication using LBP-based perceptual image hashing

Feature extraction is a main step in all perceptual image hashing schemes in which robust features will led to better results in perceptual robustness. Simplicity, discriminative power, computational efficiency and robustness to illumination changes are counted as distinguished properties of Local Binary Pattern features. In this paper, we investigate the use of local binary patterns for perceptual image hashing. In feature extraction, we propose to use both sign and magnitude information of local differences. So, the algorithm utilizes a combination of gradient-based and LBP-based descriptors for feature extraction. To provide security needs, two secret keys are incorporated in feature extraction and hash generation steps. Performance of the proposed hashing method is evaluated with an important application in perceptual image hashing scheme: image authentication. Experiments are conducted to show that the present method has acceptable robustness against perceptual content-preserving manipulations. Moreover, the proposed method has this capability to localize the tampering area, which is not possible in all hashing schemes

Investigating of Tomato Pastes Microbial Contamination in Iran and Isolation and Identification of Alicyclobacillus acidocaldarius by PCR Method

Introduction Tomato paste is one of the processed tomato products that has a long shelf life and is used as an important food ingredient all over the world. According to global statistics, Iran is among the top ten producers of tomato paste in the world, Iran ranks fourth to fifth in the world in the field of aseptic paste production. Alicyclobacillus bacteria are considered as a risk for pasteurized acidic food industries. These bacteria enter the product through soil-contaminated fruits, production equipment of the factories and finally produce metabolites such as guaiacol, causing an unpleasant taste in the product.   Materials and Methods  In order to investigate the microbial contamination of canned tomato paste in the country, 46 samples of canned tomato paste in the amount of 184 cans of 800 grams were purchased from the market. Regarding the purchase of samples from the market, we tried to buy a different production date and production series for each sample (approximately 4 cans for each brand from each production series). The purchased samples were sent to the Microbiology Department of the Standard Research Institute laboratory for microbiology tests. At the same time, the culture media of thermophilic bacteria (Orange Serum Agar, Thermoacidurans Agar from 4 available brands) were tested for performance control. The canned tomato paste samples were incubated at 30°C ± 1°C for 14 days and 55°C ± 1°C for 7 days.   Results and Discussion  The contents of both examined samples were tested separately for thermophilic bacteria, mesophilic bacteria, mold and yeast. Out of the 46 samples prepared with different production dates and production series, which were 46 cans of tomato paste, 28 samples were positive in terms of contamination with thermophilic bacteria. According to the number of contaminated samples, it was found that 60.86% of the samples were contaminated. Colonies grown on Thermoacidurans Agar medium were examined morphologically. For further investigations, gram staining was performed. All the stained colonies morphologically showed the form of gram-positive rod-shaped bacilli. Biochemical tests including catalase and oxidase were performed to identify Alicyclobacillus species. All the grown colonies were catalase positive and oxidase negative. The final identification of the species was done by performing molecular tests based on specific primers designed from Alicyclobacillus gene. These tests were performed in three stages: genomic DNA extraction, polymerase chain reaction and electrophoresis. Using the PCR method, the grown colonies were analyzed for two types of bacteria, Alicyclobacillus acidocaldarius and Bacillus coagulans. According to the results obtained from sequencing with designed primers in the NCBI database, it showed 100% similarity with the registered sequences, which are all different strains of the Alicyclobacillus acidocaldarius species. None of the colonies were detected as Bacillus coagulans species. Since Alicyclobacillus acidocaldarius was isolated from soil for the first time, the presence of these bacteria in the product indicates the contamination of raw materials with soil.   Conclusion In this research, the presence of Alicyclobacillus bacteria in canned tomato paste was confirmed. Due to the high heat resistance of this bacteria, there is a possibility of the presence of Alicyclobacillus in the all stages of tomato paste production, which have entered the product through the soil, and 95°C ± 3°C pasteurization temperature in 30 minutes is not effective in removing this bacteria completely. Most acidophilus thermophilic bacteria, such as Alicyclobacillus family, are not pathogenic bacteria. Their presence in food may make the food taste bad or smelly, but it does not pose a risk to the health of the consumer. Therefore, in order to reduce the risk of spoilage and to prevent the growth of bacterial spores in the product, it is essential not to expose the product to high temperatures for a long time. It is also necessary to perform rapid cooling after heat treatment and keep the product at a temperature below 30°C.   Acknowledgement This article is the result of a common research project of Microbiology and Biology Research Group of Standard Research Institute and Kermanshah Standard Regional Research Group. We hereby thank and appreciate the cooperation of the microbiology research group of the Standard Research Institute and the Kermanshah General Directorate of Standards. We are also very grateful to Rogin Talk Company as the employer of this project

Implementation of a novel multi-agent system for demand response management in low-voltage distribution networks

In this era of advanced distribution automation technologies, demand response is becoming an important tool for electricity network management. The available flexible loads can efficiently help in alleviating the network constraints and achieving demand-supply balance. Therefore, this forms the rationale behind this paper, which aims to implement a multi-agent system framework in order to achieve flexible price-based demand response. A genetic algorithm-based multi-objective optimization technique is applied to determine the optimal locations and the amount of required demand reduction in order to keep the network within statutory limits. The methodology is based on probabilistic estimation of the granularity of total available flexible demand from shiftable home appliances in each low-voltage feeder. Moreover, an optimal decision making for the start time of appliances upon receiving a real-time price signal is proposed. This is accomplished by considering the willingness to participate as well as price demand elasticity of the different clusters of customers. To fully demonstrate the feasibility and effectiveness of the proposed framework, a modified IEEE 69 bus distribution network comprising 1824 low voltage residential customers has been implemented and analyzed

Intuitionistic Fuzzy Time Series Functions Approach for Time Series Forecasting

Fuzzy inference systems have been commonly used for time series forecasting in the literature. Adaptive network fuzzy inference system, fuzzy time series approaches and fuzzy regression functions approaches are popular among fuzzy inference systems. In recent years, intuitionistic fuzzy sets have been preferred in the fuzzy modeling and new fuzzy inference systems have been proposed based on intuitionistic fuzzy sets. In this paper, a new intuitionistic fuzzy regression functions approach is proposed based on intuitionistic fuzzy sets for forecasting purpose. This new inference system is called an intuitionistic fuzzy time series functions approach. The contribution of the paper is proposing a new intuitionistic fuzzy inference system. To evaluate the performance of intuitionistic fuzzy time series functions, twenty-three real-world time series data sets are analyzed. The results obtained from the intuitionistic fuzzy time series functions approach are compared with some other methods according to a root mean square error and mean absolute percentage error criteria. The proposed method has superior forecasting performance among all methods

Green efficiency performance analysis of the logistics industry in China: based on a kind of machine learning methods

This paper aims to analyze the green efficiency performance of the logistics industry in China’s 30 provinces from 2008 to 2017. We first evaluate the green efficiency of the logistics industry through the non-directional distance function (NDDF) method. Then, we use the functional clustering method funHDDC, which is one of the popular machine learning methods, to divide 30 provinces into 4 clusters and analyze the similarities and differences in green efficiency performance patterns among different groups. Further, we explore the driving factors of dynamic changes in green efficiency through the decomposition method. The main conclusions of this paper are as follows: (1) In general, the level of green efficiency is closely related to the geographical location. From the clustering results, we can find that most of the eastern regions belong to the cluster with higher green efficiency, while most of the western regions belong to the cluster with lower green efficiency. However, the green efficiency performance in several regions with high economic levels, such as Beijing and Shanghai, is not satisfactory. (2) Based on the analysis of decomposition results, the innovation effect of China’s logistics industry is the most obvious, but the efficiency change still needs to be improved, and technical leadership should be strengthened. Based on these conclusions, we further propose some policy recommendations for the green development of the logistics industry in China

Consumer-Led Power Management in Local Distribution Networks

The centralised electricity generation system is now moving towards localised and distributed energy sources. The penetration of new renewable energies along with the introduction of new loads to the power grid cause several challenges in the future of distribution networks. This paper aims to investigate how effectively local energy community’s engagement can contribute in managing the demand-supply balance in distribution networks. For this purpose, an advanced demand response scheme is introduced where households within a local energy community can collaborate with each other to reduce the total energy demand. The proposed scheme is implemented using a multi agent system framework and in a modified IEEE 69 bus radial network. The simulation results demonstrated that the overall community independency to the power grid can significantly decrease specifically during PV generation perio

Novel model for defining electricity tariffs using residential load profile characterisation

In recent years, great attention towards enabling future smart grid functionalities in distribution networks creates significant changes in electricity load profiles. These changes require a new advanced paradigm in electricity pricing and tariffs definition aiming to increase the potential of responsiveness demand. This paper aims to propose a new model for designing residential electricity tariffs using their load profile clustering and characterization, taking into account social, technical and educational factors alongside the financial incentives. The methodology is based on estimation of the potential of demand responsiveness to dynamic pricing over time for different cluster of households with similar electricity consumption patterns. In this regard, different time of use pricing bands has been considered and compared with fixed price tariffs. Moreover, a baseline demand for each cluster of households has been determined based on the elasticity of demand to different static tariffs. The results show that the proposed model can provide distribution network operators (DNOs) and suppliers with a basic and general outline of defining more effective dynamic pricing schemes that reflect intermittent nature of residential demand