Event Effects Estimation on Electricity Demand Forecasting

Machine Learning Short-Term Energy Consumption Forecasting for Microgrids in a Manufacturing Plant

Energy production and supply are important challenges for civilisation. Renewable energy sources present an increased share of the energy supply. Under these circumstances, small-scale grids operating in small areas as fully functioning energy systems are becoming an interesting solution. One crucial element to the success of micro-grid structures is the accurate forecasting of energy consumption by large customers, such as factories. This study aimed to develop a universal forecasting tool for energy consumption by end-use consumers. The tool estimates energy use based on real energy-consumption data obtained from a factory or a production machine. This model allows the end-users to be equipped with an energy demand prediction, enabling them to participate more effectively in the smart grid energy market. A single, long short-term memory (LSTM)-layer-based artificial neural network model for short-term energy demand prediction was developed. The model was based on a manufacturing plant’s energy consumption data. The model is characterised by high prediction capability, and it predicted energy consumption, with a mean absolute error value of 0.0464. The developed model was compared with two other methodologies.

Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in China

In order to implement the national need for the optimal allocation of power resources, power load forecasting, as an important research topic, has important theoretical and practical significance. The purpose of this study is to construct a prediction model considering climate factors based on a large amount of historical data, and to prove that the prediction accuracy is related to both climate factors and load regularity. The results of load forecasting are affected by many climate factors, so firstly the climate variables affecting load forecasting are screened. Secondly, a load prediction model based on the IPSO-Elman network learning algorithm is constructed by taking the difference between the predicted value of the neural network and the actual value as the fitness function of particle swarm optimization. In view of the great influence of weights and thresholds on the prediction accuracy of the Elman neural network, the particle swarm optimization algorithm (PSO) is used to optimize parameters in order to improve the prediction accuracy of ELMAN neural network. Thirdly, prediction with and without climate factors is compared and analyzed, and the prediction accuracy of the model compared by using cosine distance and various error indicators. Finally, the stability discriminant index of historical load regularity is introduced to prove that the accuracy of the prediction model is related to the regularity of historical load in the forecast area. The prediction method proposed in this paper can provide reference for power system scheduling.