In the deregulated economy, the maximum load forecasting is important for the electric industry. Many applications are included such as the energy generation and purchasing. The aim of the present study is to find the...In the deregulated economy, the maximum load forecasting is important for the electric industry. Many applications are included such as the energy generation and purchasing. The aim of the present study is to find the most suitable models for the peak load of the Kingdom of Bahrain. Many mathematical methods have been developing for maximum load forecasting. In the present paper, the modeling of the maximum load, population and GDP （gross domestic product） versus years obtained. The curve fitting technique used to find that models, where Graph 4.4.2 as a tool used to find the models. As well, Neuro-Fuzzy used to find the three models. Therefore, three techniques are used. These three are exponential, linear modeling and Neuro-Fuzzy. It is found that, the Neuro-Fuzzy is the most suitable and realistic one. Then, the linear modeling is the next suitable one.展开更多
Load forecasting is a critical issue for operational planning as well as grid expansion to ensure an uninterruptable electric power system. Being a small but densely populated country in South Asia, Bangladesh has man...Load forecasting is a critical issue for operational planning as well as grid expansion to ensure an uninterruptable electric power system. Being a small but densely populated country in South Asia, Bangladesh has many isolated places which are not connected to national grid yet. If concern authority opts to expand grid to those areas, they need reliable demand data for designing and dimensioning of different power system entities, e.g., capacity, overhead line capacity, tie line capacity, spinning reserve, load-shedding scheduling, etc., for reliable operation and to prevent possible obligatory redesigning. This paper represents an analysis to forecast the electricity demand of an isolated island in Bangladesh where past history of electrical load demand is not available. The analysis is based on the identification of factors, e.g., population, literacy rate, per capita income, occupation, communication, etc., on which electrical load growth of an area depends. Data has been collected from the targeted isolated area and form a grid connected area which is similar to target area from social and geographical perspective. Weights of those factors on load have been calculated by matrix inversion. Demand of the new area is forecasted using these weights factors by matrix multiplication.展开更多
文摘In the deregulated economy, the maximum load forecasting is important for the electric industry. Many applications are included such as the energy generation and purchasing. The aim of the present study is to find the most suitable models for the peak load of the Kingdom of Bahrain. Many mathematical methods have been developing for maximum load forecasting. In the present paper, the modeling of the maximum load, population and GDP （gross domestic product） versus years obtained. The curve fitting technique used to find that models, where Graph 4.4.2 as a tool used to find the models. As well, Neuro-Fuzzy used to find the three models. Therefore, three techniques are used. These three are exponential, linear modeling and Neuro-Fuzzy. It is found that, the Neuro-Fuzzy is the most suitable and realistic one. Then, the linear modeling is the next suitable one.
文摘Load forecasting is a critical issue for operational planning as well as grid expansion to ensure an uninterruptable electric power system. Being a small but densely populated country in South Asia, Bangladesh has many isolated places which are not connected to national grid yet. If concern authority opts to expand grid to those areas, they need reliable demand data for designing and dimensioning of different power system entities, e.g., capacity, overhead line capacity, tie line capacity, spinning reserve, load-shedding scheduling, etc., for reliable operation and to prevent possible obligatory redesigning. This paper represents an analysis to forecast the electricity demand of an isolated island in Bangladesh where past history of electrical load demand is not available. The analysis is based on the identification of factors, e.g., population, literacy rate, per capita income, occupation, communication, etc., on which electrical load growth of an area depends. Data has been collected from the targeted isolated area and form a grid connected area which is similar to target area from social and geographical perspective. Weights of those factors on load have been calculated by matrix inversion. Demand of the new area is forecasted using these weights factors by matrix multiplication.
