In the smart grid (SG) environment, consumers are enabled to alter electricity consumption patterns in response to electricity prices and incentives. This results in prices that may differ from the initial price pattern. Electricity price and demand forecasting play a vital role in the reliability and sustainability of SG. Forecasting using big data has become a new hot research topic as a massive amount of data is being generated and stored in the SG environment. Electricity users, having advanced knowledge of prices and demand of electricity, can manage their load efficiently. In this paper, a recurrent neural network (RNN), long short term memory (LSTM), is used for electricity price and demand forecasting using big data. Researchers are working actively to propose new models of forecasting. These models contain a single input variable as well as multiple variables. From the literature, we observed that the use of multiple variables enhances the forecasting accuracy. Hence, our proposed model uses multiple variables as input and forecasts the future values of electricity demand and price. The hyperparameters of this algorithm are tuned using the Jaya optimization algorithm to improve the forecasting ability and increase the training mechanism of the model. Parameter tuning is necessary because the performance of a forecasting model depends on the values of these parameters. Selection of inappropriate values can result in inaccurate forecasting. So, integration of an optimization method improves the forecasting accuracy with minimum user efforts. For efficient forecasting, data is preprocessed and cleaned from missing values and outliers, using the z-score method. Furthermore, data is normalized before forecasting. The forecasting accuracy of the proposed model is evaluated using the root mean square error (RMSE) and mean absolute error (MAE). For a fair comparison, the proposed forecasting model is compared with univariate LSTM and support vector machine (SVM). The values of the performance metrics depict that the proposed model has higher accuracy than SVM and univariate LSTM.

In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud and anomalies. Moreover, blockchain technology is being introduced as the most secure method integrated into finance. However, along with these advanced technologies, many frauds are also increasing every year. Therefore, we propose a secure fraud detection model based on machine learning and blockchain. There are two machine learning algorithms—XGboost and random forest (RF)—used for transaction classification. The machine learning techniques train the dataset based on the fraudulent and integrated transaction patterns and predict the new incoming transactions. The blockchain technology is integrated with machine learning algorithms to detect fraudulent transactions in the Bitcoin network. In the proposed model, XGboost and random forest (RF) algorithms are used to classify transactions and predict transaction patterns. We also calculate the precision and AUC of the models to measure the accuracy. A security analysis of the proposed smart contract is also performed to show the robustness of our system. In addition, an attacker model is also proposed to protect the proposed system from attacks and vulnerabilities.

No abstract

No abstract

No abstract