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Forecasting the annual long-term consumption of electrical energy in a country has remained for the Electrical Engineer quite a difficult problem to solve. As an important planning tool, the forecast of electrical energy consumption has to be as precise as possible. The most commonly employed method is that of scenario building. With the scenario method, consumption forecasting is done through the simulation of a sequence of events. The generated data cannot therefore be as stochastic as it is in reality. With the advent of the computer age, numerous other statistical methods for consumption forecasting have been developed. Prominent among them is the forecasting by machine learning with multiple-Input multiple-Output local learning strategy. The objective here is to obtain a forecast which is as precise as possible, while conserving the stochastic nature between the historical and the forecasted data. This article first presents the different strategies for long-term power consumption forecasting using Multiple-Input Multiple-Output local learning strategies. It then proposes, based on the work of earlier researchers, an approach that uses the weighted averages to improve on the level of precision obtained. Furthermore, it applies this new improved calculation method to forecast the power consumption specifically in Cameroon for horizon 2035, when the country aspires to become an emerging economy. The last part of this article utilizes historical data on the electricity consumption of some countries from the World Bank dataset to do a comparative study between the here newly proposed method and that used previously. The results show that, the new method using MISMO plus weighted average delivers more exact results for long-term electrical power consumption forecasts. [ABSTRACT FROM AUTHOR]

In the modern context of electricity market deregulation, the price of the kilowatt-hour must take both power injections and withdrawals of the multiple market participants into consideration, as well as their actual grid usage. The responsibility for causing transmission losses and voltage drops therefore, needs to be fairly attributed. While grid power injections and withdrawals are unequivocally attributed, it remains to date impossibly to naturally share responsibility for transmission losses. Relevant literature proposes a variety of methods. This paper proposes a new method for allocating transmission losses to market participants by using the network. The overall grid losses are obtained from summing the difference between injected and withdrawn power for all nodes. A set of allocation factors derived from the electrical distance between concerned buses and their voltage levels that is used to attribute active power loss to each bus, after the losses which are arising from the mutual influencing between buses has been calculated. This method focuses on bus bar current injections and it assumes which there is a hypothetical power flow between nodes. For mutual influencing, one of the bus bars is considered a generator and the other is a load. A reference bus voltage is set and then the load side is penalized depending on how far its own voltage is lower than that reference value. Results from a sample network are compared to those of previous methods. [ABSTRACT FROM AUTHOR]