Demand side load management is a means of using existing energy production facilities more efficiently by reducing price volatility and improving electric grid reliability. Electricity is not steady, varying along a range of different timeframes.Increas ed demand for energy during the peak hours, in particular, puts a strain on the transmission and distribution systems.DSM consists of three main categories: DSM policies, DSM measures and DSM strategies. DSM Categories includes energy efficiency (efficiency, conservation), demand side response (price -based, incentive payment-based) and on-site back-up (generation, storage). DSM policies can be regulatory, market-based, voluntary and financial. DSM implementers are utility, customer, network operator, aggregator and government. This research aims to identifying a long term load pattern and price forecasting for monthly energy consumption demand for Addi s Ababa city residential customer. This forecast method uses artificial neural network algorithm based on feed forward back propagation algorithm.Final, from this research will be expect understand the different types of demand-side management evaluates supply side and demand side impact on all over the grid system. The research will show that demand side management strategies apply on the residential customer could result significant reductionon total electricity consumptionat peak loads shift to off peak load or using other types of demand-side management program. This will be reducing electricity price, increase value service andconserve energy. Keywords:Demand side load management (DSM),artificial neural network,forecast,algorithm