Electricity demand forecasting has been a real challenge for power system scheduling in different levels of energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for short-term load forecasting, although scant evidence is available about the feasibility of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationales behind short-term load forecasting methodologies based on works of previous researchers in the energy field. Fundamental benefits and drawbacks of these methods are discussed to represent the efficiency of each approach in various circumstances. Finally, a hybrid strategy is proposed.

This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.

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In this paper, the lightning protection requirements of a typical residential building have been discussed and techniques have been provided to protect the building from both direct and indirect damages of lightning, with special attention to the protection of PV panels placed on the roof. These techniques include the designing challenges and also the type of devices which can be used to reduce the surge current flow and magnetic field. It has been shown that for buildings with roof top PV systems only the avoidance of lightning attachment to unprotected parts of the building is not sufficient. Lightning currents passing through the lightning protection system may still affect the PV power system through inductive coupling. Hence strategic placement of PV systems and shielding of conducting systems wherever possible has been recommended. It has also been envisaged that the impact of lightning on PV systems is directly related to the isokeraunic level of the region and elevation of the building. Several recommendations have been proposed in designing the air termination system for a roof with PV panels in high isokeraunic regions. Finally the building integrated photo voltaic (BIPV) projects which are conducted in Malaysia have been evaluated..