Power load forecasting

For the electric power factory, the power load forecasting problem, including load forecasting and consumption predicting, is crucial to work planning. According to the predicting time, it can be divided into long-term forecasting, mid-term forecasting, short-term forecasting and ultra-short-term forecasting. The long-term and mid-term forecasting are mainly used for macro control, and their forecasting time arrange are from one year to ten years and from one month to twelve months respectively. The short-term forecasting which prediction time is from one day to seven days is used in generators macroeconomic control, power exchange plan and some other areas. Predicting the situation in next 24 hours is named as the ultra-short-term forecasting which is used for failure prediction, emergency treatment and frequency control. In general, the forecast accuracy is different for different prediction time. The longer is the time, the lower accurate is the prediction. As the unique power supplier in Huizhou (China), Huizhou Electric Power wants to know the solution to the problems: 1. Prediction of the total electrical consumption and the peak load of the city in 2006 based on the economy development and the feature of the city. 2. Monthly prediction of the consumption and peak load in 2006. 3. Daily prediction of the consumption and peak load from July 10th to 16th in 2006. 4. Prediction of the load every 15 minutes of July 10th. 5. Real-time forecasting which means to amend the existing load prediction for next 15 minute

Short-Term Load Forecasting: The Similar Shape Functional Time Series Predictor

We introduce a novel functional time series methodology for short-term load forecasting. The prediction is performed by means of a weighted average of past daily load segments, the shape of which is similar to the expected shape of the load segment to be predicted. The past load segments are identified from the available history of the observed load segments by means of their closeness to a so-called reference load segment, the later being selected in a manner that captures the expected qualitative and quantitative characteristics of the load segment to be predicted. Weak consistency of the suggested functional similar shape predictor is established. As an illustration, we apply the suggested functional time series forecasting methodology to historical daily load data in Cyprus and compare its performance to that of a recently proposed alternative functional time series methodology for short-term load forecasting.Comment: 22 pages, 6 Figures, 1 Tabl

Local Short Term Electricity Load Forecasting: Automatic Approaches

Short-Term Load Forecasting (STLF) is a fundamental component in the efficient management of power systems, which has been studied intensively over the past 50 years. The emerging development of smart grid technologies is posing new challenges as well as opportunities to STLF. Load data, collected at higher geographical granularity and frequency through thousands of smart meters, allows us to build a more accurate local load forecasting model, which is essential for local optimization of power load through demand side management. With this paper, we show how several existing approaches for STLF are not applicable on local load forecasting, either because of long training time, unstable optimization process, or sensitivity to hyper-parameters. Accordingly, we select five models suitable for local STFL, which can be trained on different time-series with limited intervention from the user. The experiment, which consists of 40 time-series collected at different locations and aggregation levels, revealed that yearly pattern and temperature information are only useful for high aggregation level STLF. On local STLF task, the modified version of double seasonal Holt-Winter proposed in this paper performs relatively well with only 3 months of training data, compared to more complex methods

Analysis load forecasting of power system using fuzzy logic and artificial neural network

Load forecasting is a vital element in the energy management of function and execution purpose throughout the energy power system. Power systems problems are complicated to solve because power systems are huge complex graphically widely distributed and are influenced by many unexpected events. This paper presents the analysis of load forecasting using fuzzy logic (FL), artificial neural network (ANN) and ANFIS. These techniques are utilized for both short term and long-term load forecasting. ANN and ANFIS are used to improve the results obtained through the FL. It also studied the effects of humidity, temperature and previous load on Load Forecasting. The simulation is done by the Simulink environment of MATLAB software

Exploiting road traffic data for very short term load forecasting in smart grids

If accurate short term prediction of electricity consumption is available, the Smart Grid infrastructure can rapidly and reliably react to changing conditions. The economic importance of accurate predictions justifies research for more complex forecasting algorithms. This paper proposes road traffic data as a new input dimension that can help improve very short term load forecasting. We explore the dependencies between power demand and road traffic data and evaluate the predictive power of the added dimension compared with other common features, such as historical load and temperature profiles