Short Term Load Forecasting Using Deep Neural Networks
Electricity load forecasting plays an important role in the energy planning such as generation and distribution. However, the nonlinearity and dynamic uncertainties in the smart grid environment are the main obstacles in forecasting accuracy. Deep Neural Network (DNN) is a set of intelligent computational algorithms that provide a comprehensive solution for modelling a complicated nonlinear relationship between the input and output through multiple hidden layers. In this paper, we propose DNN based electricity load forecasting system to manage the energy consumption in an efficient manner. We investigate the applicability of two deep neural network architectures Feed-forward Deep Neural Network (Deep-FNN) and Recurrent Deep Neural Network (Deep-RNN) to the New York Independent System Operator (NYISO) electricity load forecasting task. We test our algorithm with various activation functions such as Sigmoid, Hyperbolic Tangent (tanh) and Rectifier Linear Unit (ReLU). The performance measurement of two network architectures is compared in terms of Mean Absolute Percentage Error (MAPE) metric.
Code (0)
등록된 구현이 없습니다.
Tasks
Load ForecastingSimilar Papers 제목 키워드 기반
Short-Term Load Forecasting Using AMI Data
Accurate short-term load forecasting is essential for the efficient operation of the power sector. Forecasting load at a fine granularity such as hourly loads of individual households is challenging due to higher volatil…
Load ForecastingShort-term load forecasting using optimized LSTM networks based on EMD
Short-term load forecasting is one of the crucial sections in smart grid. Precise forecasting enables system operators to make reliable unit commitment and power dispatching decisions. With the advent of big data, a numb…
Load ForecastingTime SeriesTime Series AnalysisForecasting Short-term load using Econometrics time series model with T-student Distribution
By significant improvements in modern electrical systems, planning for unit commitment and power dispatching of them are two big concerns between the researchers. Short-term load forecasting plays a significant role in p…
EconometricsLoad ForecastingTime SeriesTime Series Analysis+1Stacked Boosters Network Architecture for Short Term Load Forecasting in Buildings
This paper presents a novel deep learning architecture for short term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a sin…
Load ForecastingTime SeriesTime Series AnalysisAppliance Level Short-term Load Forecasting via Recurrent Neural Network
Accurate load forecasting is critical for electricity market operations and other real-time decision-making tasks in power systems. This paper considers the short-term load forecasting (STLF) problem for residential cust…
Decision MakingLoad ForecastingPrediction