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Power transmission lines in forest regions like the southern parts of Cameroon are subjected to numerous failures arising from natural hazards, including earth faults and line ruptures provoked by swaying or falling neighbouring trees and their branches. To pre-empt this problem, those trees which represent a potential threat to the operation of the power line must be eliminated. Deforestation during line construction phase therefore becomes inevitable, and this leads in turn to the destruction of flora and fauna. The need hence arises to implement a deforestation strategy during power line construction which limits the negative impact of loss of forestry and wildlife resources on the environment to an acceptable level. In this paper a method is proposed which limits the level of destruction of vegetation and respects modern environmental standards during the construction of power lines through dense forest regions. It is shown that the required right-of-way depends on the quantity of power to be transmitted, on the voltage level chosen for the transmission, on the type of accessories used for the line construction and on the relief of the line track. Consequently, the relevant parameters for deforestation have been identified, listed and analysed. This leads to a good overview of the required deforestation level in the design and realisation of a power transmission line. The environmental impact assessment of transmission line projects can hence be better quantified and compared in aspects that relate to the protection of trees in the fight against global warming and desertification. Key words: Power line construction, right-of-way, deforestation, environmental impact, global warming, desertification

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.