%0 Journal Article %1 DeFelice2015Seasonal %A De Felice, Matteo %A Alessandri, Andrea %A Catalano, Franco %D 2015 %J Applied Energy %K MyECEMpaper climateservices demand energy seasonal %P 435--444 %R 10.1016/j.apenergy.2014.10.030 %T Seasonal climate forecasts for medium-term electricity demand forecasting %U http://dx.doi.org/10.1016/j.apenergy.2014.10.030 %V 137 %X Highlights • During the ten years, seasonal climate forecasts have improved their skill. • We analyzed the link between summer average temperature and demand over Italy. • Both deterministic and probabilistic forecasting approaches are here considered. • Climate forecasts show a significant skill in predicting the demand in many regions. Abstract Air temperature is an effective predictor for electricity demand, especially during hot periods where the need of electric air conditioning can be high. This paper presents for the first time an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction. The retrospective seasonal climate forecasts provided by ECWMF (European Centre for Medium-Range Weather Forecasts) are used to forecast the June–July Italian electricity demand for the period 1990–2007. We find a relationship between summer (June–July) average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach. With the aim to evaluate the potential usefulness of the information contained into the climate ensemble forecast, the analysis is extended considering a probabilistic approach. Results show that, especially in the Center-South of Italy, seasonal forecasts of temperature issued in May lead to a significant correlation coefficient of electricity demand greater than 0.6 for the summer period. The average correlation obtained from seasonal forecasts is 0.53 for the temperature predicted in May and 0.19 for the predictions issued in April for the linear model, while the non-linear approach leads to the coefficients of 0.62 and 0.36 respectively. For the probabilistic approach, seasonal forecasts exhibit a positive and significant skill-score in predicting the demand above/below the upper/lower tercile in many regions. This work is a significant progress in understanding the relationship between temperature and electricity demand. It is shown that much of the predictable electricity demand anomaly over Italy is connected with so-called heat-waves (i.e. long lasting positive temperature anomalies) over Europe.

@article{DeFelice2015Seasonal, abstract = {Highlights • During the ten years, seasonal climate forecasts have improved their skill. • We analyzed the link between summer average temperature and demand over Italy. • Both deterministic and probabilistic forecasting approaches are here considered. • Climate forecasts show a significant skill in predicting the demand in many regions. Abstract Air temperature is an effective predictor for electricity demand, especially during hot periods where the need of electric air conditioning can be high. This paper presents for the first time an assessment of the use of seasonal climate forecasts of temperature for medium-term electricity demand prediction. The retrospective seasonal climate forecasts provided by ECWMF (European Centre for Medium-Range Weather Forecasts) are used to forecast the June–July Italian electricity demand for the period 1990–2007. We find a relationship between summer (June–July) average temperature patterns over Europe and Italian electricity demand using both a linear and non-linear regression approach. With the aim to evaluate the potential usefulness of the information contained into the climate ensemble forecast, the analysis is extended considering a probabilistic approach. Results show that, especially in the Center-South of Italy, seasonal forecasts of temperature issued in May lead to a significant correlation coefficient of electricity demand greater than 0.6 for the summer period. The average correlation obtained from seasonal forecasts is 0.53 for the temperature predicted in May and 0.19 for the predictions issued in April for the linear model, while the non-linear approach leads to the coefficients of 0.62 and 0.36 respectively. For the probabilistic approach, seasonal forecasts exhibit a positive and significant skill-score in predicting the demand above/below the upper/lower tercile in many regions. This work is a significant progress in understanding the relationship between temperature and electricity demand. It is shown that much of the predictable electricity demand anomaly over Italy is connected with so-called heat-waves (i.e. long lasting positive temperature anomalies) over Europe.}, added-at = {2018-06-18T21:23:34.000+0200}, author = {De Felice, Matteo and Alessandri, Andrea and Catalano, Franco}, biburl = {https://www.bibsonomy.org/bibtex/202148b720b37ad6798ecfa85da6f51ee/pbett}, citeulike-article-id = {13422877}, citeulike-attachment-1 = {DeFelice-APEN-2015.pdf; /pdf/user/pbett/article/13422877/1027328/DeFelice-APEN-2015.pdf; 0104a864be9b3eaeb6c0a8b471a1a4daf8d22cf1}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.apenergy.2014.10.030}, doi = {10.1016/j.apenergy.2014.10.030}, file = {DeFelice-APEN-2015.pdf}, interhash = {a9e996eb3de1a3ed0a22fc44624b23a6}, intrahash = {02148b720b37ad6798ecfa85da6f51ee}, issn = {03062619}, journal = {Applied Energy}, keywords = {MyECEMpaper climateservices demand energy seasonal}, month = jan, pages = {435--444}, posted-at = {2014-11-08 15:27:08}, priority = {2}, timestamp = {2018-10-08T18:26:55.000+0200}, title = {Seasonal climate forecasts for medium-term electricity demand forecasting}, url = {http://dx.doi.org/10.1016/j.apenergy.2014.10.030}, volume = 137, year = 2015 }