Short term load forecasting using particle swarm optimization neural network

Energy is very important in many areas of life. Moreover, humans seem to be almost totally reliant on electrical energy in the last few decades. Although, huge efforts are invested in electronic devices which consume lesser energy or rely on alternative power sources, many emerging devices continue to rely on some sort of electrical power. Energy companies are tasked with supplying sufficient energy to consumers; hence, such companies should be able to project the amount of energy to be made available to consumers at different times. It is undesirable that lesser energy than demanded is supplied at any particular time, as this may lead to system collapse or compulsory shedding of load (some consumers experience power interruption). In this work, we model the problem of short term load forecasting using particle swarm optimized feedforward neural network. The described system is capable of predicting hourly load supplied by an energy company. Also, we investigate modeling load forecasting with conventional feedforward neural network, trained with the back propagation learning algorithm. The results obtained show that the both particle swarm and back propagation optimized feedforward networks are suitable regressors for modeling energy demand. Although, the back propagation optimized networks slightly edge on achieved mean absolute error (MAE) and mean square (MSE), the particle swarm optimized networks boasts of faster convergence. Training is roughly twice fast in particle swarm optimized networks, since error gradients computations are not required for optimization. The database used within this work is obtained from a North Cyprus based energy company.