Relationships between meteorological variables and monthly electricity demand
Applied Energy, (October 2012)DOI: 10.1016/j.apenergy.2012.03.053
Abstract
Electricity demand depends on climatic condition and the influence of weather has been widely reported in the past. The main purpose of this study is to analyse the effect of the meteorological variability on the monthly electricity demand in Italy. Temperature, wind speed, relative humidity and cloud cover are considered; the calendar effect is also taken into account. A multiple linear regression model based on calendar and weather related variables is developed to study the relationships between meteorological variables and electricity demand as well as to predict the monthly electricity demand up to 1 month ahead. The model has been extensively tested over the period 1994–2009 using different combinations of the weather related variables. Accuracies obtained are quite similar and range between 0.85\% and 0.89\%. Temperature turns out to be the most important variable. According to the month considered, a specific combination of the weather related variables can give the lowest Mean Absolute Percentage Error (MAPE) but differences are usually small. Good results for the summer months are obtained using Heat Index to calculate the Cooling Degree-Days; the cloud cover has a major influence from February to April. When demand forecasts are performed using the predicted meteorological variables, an overall accuracy (MAPE) around 1.3\% is obtained over the period 1994–2009. The proposed model clearly identifies the influence of the weather conditions on the aggregated national electricity demand. ⺠Weather and calendar variables effects on monthly electricity demand. ⺠Multiple linear regression model for monthly electricity demand forecast. ⺠Temperature effect evaluation by means of heating and cooling degree days. ⺠Good meteorological variable estimates highly improve monthly demand forecast. ⺠Forecast monthly Mean Absolute Percentage Error (MAPE) around 1.3\%.