Load forecasting is an essential operation in the power utility industry. However, a common challenge is faced for adjusting forecasting models to fit the need for substations' load prediction as well as minimizing expenditure in IT resources for repurposing these forecasting models to bigger datasets. The goal of this paper is to propose a novel solution that is responsive to these demands through the integration of reinforcement learning with load forecasting on existing database technology. To deal with the varying accuracy of the forecasting models on different substations' loads, the proposed solution compares and uses the best models and recalibrate them iteratively by comparing the model's prediction against the actual load data. As shown in empirical analysis, the solution interacts with the environment and performs the optimum forecasting routine intuitively.

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In this paper, modeling and simulation of 1MW grid connected PV system is simulated using National Renewable Energy Laboratory's (NREL) HOMER software, and the optimum system is analyzed to see the economic feasibility of the system in a small industry area in Malacca, Malaysia. The system is expected to foresee reduced grid energy consumption. Emphasis is also placed on reduction of green house gases emission. HOMER will simulate the system and perform optimization of system according to the available usage data and the available renewable energy (sun radiation) data. The lifecycle and cost of each system modules will also affect the optimization duly. In addition, HOMER also performs optimizations according to different assumption of uncertain factors to gauge the effect of sensitivity list.

Despite protected by firewalls and network security systems, databases are vulnerable to attacks especially when the perpetrators are from within the organization and have authorized access to these systems. Detecting their malicious activities is difficult as each database has its own set of unique usage activities and the generic exploitation avoidance rules are usually not applicable. This paper proposes a novel method to improve the security of a database by using machine learning to learn the user behaviour unique to a database environment and apply that learning to detect anomalous user activities through the analysis of sequences of user session data. Once these suspicious users are detected, their privileges are systematically suppressed. The empirical analysis shows that the proposed approach can intuitively adapt to any database that supports a wide variety of clients and enforce stringent control customized to the specific IT systems.

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