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.

The aim of this paper was to improve the fire retardancy of beech wood by graphene. Six fire properties, namely time to onset of ignition, time to onset of glowing, back-darkening time, back-holing time, burnt area and weight loss were measured using a newly developed apparatus with piloted ignition. A set of specimens was treated with nano-wollastonite (NW) for comparison with the results of graphene-treated specimens. Graphene and NW were mixed in a water-based paint and brushed on the front and back surface of specimens. Results demonstrated significant improving effects of graphene on times to onset of ignition and glowing. Moreover, graphene drastically decreased the burnt area. Comparison between graphene- and NW-treated specimens demonstrated the superiority of graphene in all six fire properties measured here. Fire retardancy impact of graphene was attributed to its very low reaction ability with oxygen, as well as its high and low thermal conductivity in in-plane and cross-section directions, respectively. The improved fire-retardancy properties by the addition of graphene in paint implied its effectiveness in hindering the spread of fire in buildings and structures, providing a longer timespan to extinguish a fire, and ultimately reducing the loss of life and property. Based on the improvements in fire properties achieved in graphene-treated specimens, it was concluded that graphene has a great potential to be used as a fire retardant in solid wood species.

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Electricity demand forecasting has been a real challenge for power system scheduling in the different levels of the energy sectors. Various computational intelligence techniques and methodologies have been employed in the electricity market for load forecasting; although, scant evidence is available about the feasibility of each of these methods considering the type of data and other potential factors. This work introduces several scientific, technical rationale behind intelligent forecasting methods, based on the work of previous researchers in the field of energy. The fundamental benefits and main drawbacks of the aforementioned methods are discussed in order to depict the efficiency of each approach in various situations. In the end, a proposed hybrid strategy is represented.